thesis statement for artificial intelligence

The Main Topics for Coursework or a Thesis Statement in Artificial Intelligence

Artificial Intelligence (AI) is changing the world, from machine learning and the Internet of Things to Robotics and Natural Language processing.

Research is needed to understand more about AI and how it will affect the future.

AI-powered machines are likely to replace humans in many fields and the consequences of this are still largely unknown.

There are many topics of vital importance to choose from if you’re a student trying to decide on a topic involving AI for your thesis.

Image source: Freepik.com

Machine learning (ML) as a Thesis Topic

Artificial intelligence enables machines to automatically learn a task from experience and improve performance without any human intervention.

Machines need high-quality data to start with. They are trained by building machine learning models using the data and different algorithms.

The algorithms depend on the type of data and the tasks that need automation.

A topic for your research could involve discussing wearable devices. They are powered by machine learning and are becoming increasingly popular.

You could discuss their relevance in fields like health and insurance as well as how they can help individuals to improve their daily routines and move towards a more healthy lifestyle.

Deep learning (DL) as a Thesis Topic

Deep Learning is a subset of ML where learning imitates the inner workings of the human brain. It uses artificial neural networks to process data and make decisions.

The web-like networks take a non-linear approach to processing data which is superior to traditional algorithms that take a linear approach.

Google’s RankBrain is an example of an artificial neural network.

Deep learning is driving many AI applications such as object recognition, playing computer games, controlling self-driving cars and language translation.

A research topic could involve discussing deep learning and its various applications.

Reinforcement learning (RL) as a Thesis Topic

Reinforcement learning is the closest form of learning to the way human beings learn. For instance, students learn from their mistakes and a process of trial-and-error.

There are many different ways to use AI in education to help students, such as using AI-powered tutors, customized learning and smart content.

RL works on a similar principle to learning from a process of trial-and-error. Google’s AlphaGo program beat the world champion of Go in 2017 by using RL.

Students who don’t yet have the skills to handle complex assignments can make use of various tools, writing apps and professional writers.

To find help with your student papers when you’re conducting research for a university, EduBirdie has free plagiarism checker and citations tools but professional writers who can take the pressure off you.

At U.K. EduBirdie , a professional thesis writer will finish your paper for you. It also offers editing and proofreading services at very reasonable prices.

Image source: Freepik.com

Natural language processing (NLP) as a Thesis Topic

This area of AI relates to how machines can learn to recognize and analyze human speech. Speech recognition, natural language translation and natural language generation are some of the areas of NLP.

With the help of NLP, systems can even read sentiment and predict which parts of the language are important. Revolutionary tools like IBM Watson, Google Translate, Speech Recognition and sentiment analysis show the importance of NLP in the daily lives of individuals.

NLP helps build intelligent systems, such as customer support applications like chatbots and AI in education is also a great example.

Chatbots use NLP and machine learning to interact with customers and solve their queries. Your research topic could relate to chatbots and their interaction with humans.

Computer vision (CV) as a Thesis Topic

Millions of images are uploaded daily on the internet. Computers are very good at certain tasks but they can struggle with simple tasks like being able to recognize and identify objects.

Computer vision is a field of AI that makes systems so smart that they can analyze and understand images. CV systems can even outperform humans now in some tasks like classifying visual objects.

One of the applications of computer vision is in autonomous vehicles that need to analyze images of surroundings in order to navigate.

A study topic could involve discussing computer vision and how using it allows smart systems to be built. Applications of computer vision could then be presented.

Recommender systems (RS) as a Thesis Topic

Recommender systems use algorithms to offer relevant suggestions to users. These may be suggestions on a TV show, a product, a service or even who to date.

You will receive many recommendations after you search for a particular product or browse a list of favorite movies. RS can base suggestions on your past behavior and past preferences, trends and the preferences of your peers.

A very relevant topic would be to explore the use of recommender systems in the field of e-commerce. Industry giants like Amazon are currently using recommender systems to help customers find the right products or services.

You could discuss their implementation and the type of results they bring to ecommerce businesses.

Robotics as a Thesis Topic

Robots can behave and perform the same actions as human beings, thanks to AI. They can act intelligently and even solve problems and learn in controlled environments.

For example, Kismet is a social interaction robot developed by MIT’s AI lab that can recognize human language and interact with humans.

Robots and AI are changing the way businesses work. Some people argue that this will have an adverse effect on humans as they are replaced by AI-powered machines.

A research topic could aim to understand to what extent businesses will be impacted by AI-powered machines and assess their future in different businesses.

There is an increase in the number of research papers being published in different areas of AI. If you’re a student wanting to come up with a topic involving artificial intelligence for your thesis, there are many vitally important sub-topics to choose from.

Each of these sub-topics provides plenty of opportunities for meaningful research into AI and new ideas on its application in the future as machines keep growing in intelligence.

About The Author

Paul Calderon

Paul Calderon is data security specialist working with a tech startup based in Silicon Valley. After work hours, he helps students studying for their computer science degrees or programming courses with essays, dissertations and term papers. When he isn’t doing any work, he likes playing tennis, cycling, and creating vlogs on local travel.

Most Popular

11 days ago

How to Cite Personal Communication in APA

Best summarising strategies for students, teachmeforfree review, how to cite page numbers in apa.

10 days ago

Study Reveals AI Text Detectors’ Accuracy Can Drop to 17%. What Is The Reason?

Artificial intelligence & machine learning thesis statement examples.

freepik.com

Artificial Intelligence (AI) and Machine Learning (ML) are pioneering technologies driving innovation across various sectors. When composing a thesis in this dynamic field, it is essential to commence with a concise and precise thesis statement that encapsulates your research’s essence. Below are examples of good and bad thesis statements, each followed by an analysis illustrating their effectiveness or shortcomings.

Good Thesis Statement Examples

Specific and Clear: “This thesis will investigate the application of machine learning algorithms in predicting stock prices with a focus on the technology sector.” Unclear: “Machine learning can be used to predict stock prices.”

The good example is clear and specific, detailing the application area (stock price prediction) and narrowing the focus to the technology sector. In contrast, the bad statement is vague, lacking both specificity and a defined scope.

Arguable and Debatable: “Despite its benefits, the implementation of AI in hiring processes can inadvertently reinforce existing biases, thus exacerbating workplace inequality.” Dull: “AI in hiring has pros and cons.”

The good statement is debatable and presents a clear argument, highlighting the potential downside of AI in hiring. Meanwhile, the bad statement is indecisive and fails to present a clear argument or stance.

Researchable and Measurable: “This study explores the efficacy of deep learning in the early detection of breast cancer through the analysis of mammographic images.” Uninspiring: “AI can help detect diseases early.”

A good example is researchable and measurable, specifying the AI type (deep learning), application (early detection of breast cancer), and method (analysis of mammographic images). Conversely, the bad statement is too general and lacks specificity.

Bad Thesis Statement Examples

Overly Broad: “Artificial intelligence is changing the world.”

While true, this statement is overly broad, providing no clear direction or focus for research.

Lack of Clear Argument: “AI and ML are important in data analysis.”

This statement, while factual, lacks a clear argument or focus, not providing the reader with an understanding of the research’s purpose or direction.

Unoriginal and Unengaging: “AI is used in many areas like healthcare, finance, and technology.”

Though factual, this statement is unoriginal and unengaging, lacking a specific focus or claim to guide the research.

Crafting an effective thesis statement for AI and ML research necessitates clarity, specificity, and a well-defined argument. Good thesis statements serve as a robust foundation, guiding both the researcher and the reader through the research journey. Conversely, bad thesis statements are vague, broad, and lack a clear focus, which might misguide the research process. By considering the examples provided, students can adeptly craft thesis statements that not only encapsulate their research focus but also engage readers with compelling arguments in the ever-evolving fields of Artificial Intelligence and Machine Learning.

Comments (0)

Welcome to A*Help comments!

We’re all about debate and discussion at A*Help.

We value the diverse opinions of users, so you may find points of view that you don’t agree with. And that’s cool. However, there are certain things we’re not OK with: attempts to manipulate our data in any way, for example, or the posting of discriminative, offensive, hateful, or disparaging material.

Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

More from Thesis Statement Examples and Samples

Sep 30 2023

Gender & Sexuality Studies Thesis Statement Examples

Criminal Justice Reform Thesis Statement Examples

Sustainable Development Goals (SDGs) Thesis Statement Examples

Remember Me

Is English your native language ? Yes No

What is your profession ? Student Teacher Writer Other

Forgotten Password?

Username or Email

thesis statement for artificial intelligence

Onsite training

3,000,000+ delegates

15,000+ clients

1,000+ locations

KnowledgePass
Log a ticket

01344203999 Available 24/7

12 Best Artificial Intelligence Topics for Research in 2024

Explore the "12 Best Artificial Intelligence Topics for Research in 2024." Dive into the top AI research areas, including Natural Language Processing, Computer Vision, Reinforcement Learning, Explainable AI (XAI), AI in Healthcare, Autonomous Vehicles, and AI Ethics and Bias. Stay ahead of the curve and make informed choices for your AI research endeavours.

Exclusive 40% OFF

Training Outcomes Within Your Budget!

We ensure quality, budget-alignment, and timely delivery by our expert instructors.

Share this Resource

AI Tools in Performance Marketing Training
Deep Learning Course
Natural Language Processing (NLP) Fundamentals with Python
Machine Learning Course
Duet AI for Workspace Training

Table of Contents

1) Top Artificial Intelligence Topics for Research

a) Natural Language Processing

b) Computer vision

c) Reinforcement Learning

d) Explainable AI (XAI)

e) Generative Adversarial Networks (GANs)

f) Robotics and AI

g) AI in healthcare

h) AI for social good

i) Autonomous vehicles

j) AI ethics and bias

2) Conclusion

Natural Language Processing

Natural Language Processing (NLP) is centred around empowering machines to comprehend, interpret, and even generate human language. Within this domain, three distinctive research avenues beckon:

1) Sentiment analysis: This entails the study of methodologies to decipher and discern emotions encapsulated within textual content. Understanding sentiments is pivotal in applications ranging from brand perception analysis to social media insights.

2) Language generation: Generating coherent and contextually apt text is an ongoing pursuit. Investigating mechanisms that allow machines to produce human-like narratives and responses holds immense potential across sectors.

3) Question answering systems: Constructing systems that can grasp the nuances of natural language questions and provide accurate, coherent responses is a cornerstone of NLP research. This facet has implications for knowledge dissemination, customer support, and more.

Computer Vision

Computer Vision, a discipline that bestows machines with the ability to interpret visual data, is replete with intriguing avenues for research:

1) Object detection and tracking: The development of algorithms capable of identifying and tracking objects within images and videos finds relevance in surveillance, automotive safety, and beyond.

2) Image captioning: Bridging the gap between visual and textual comprehension, this research area focuses on generating descriptive captions for images, catering to visually impaired individuals and enhancing multimedia indexing.

3) Facial recognition: Advancements in facial recognition technology hold implications for security, personalisation, and accessibility, necessitating ongoing research into accuracy and ethical considerations.

Reinforcement Learning

Reinforcement Learning revolves around training agents to make sequential decisions in order to maximise rewards. Within this realm, three prominent Artificial Intelligence Topics emerge:

1) Autonomous agents: Crafting AI agents that exhibit decision-making prowess in dynamic environments paves the way for applications like autonomous robotics and adaptive systems.

2) Deep Q-Networks (DQN): Deep Q-Networks, a class of reinforcement learning algorithms, remain under active research for refining value-based decision-making in complex scenarios.

3) Policy gradient methods: These methods, aiming to optimise policies directly, play a crucial role in fine-tuning decision-making processes across domains like gaming, finance, and robotics.

Introduction To Artificial Intelligence Training

Explainable AI (XAI)

The pursuit of Explainable AI seeks to demystify the decision-making processes of AI systems. This area comprises Artificial Intelligence Topics such as:

1) Model interpretability: Unravelling the inner workings of complex models to elucidate the factors influencing their outputs, thus fostering transparency and accountability.

2) Visualising neural networks: Transforming abstract neural network structures into visual representations aids in comprehending their functionality and behaviour.

3) Rule-based systems: Augmenting AI decision-making with interpretable, rule-based systems holds promise in domains requiring logical explanations for actions taken.

Generative Adversarial Networks (GANs)

The captivating world of Generative Adversarial Networks (GANs) unfolds through the interplay of generator and discriminator networks, birthing remarkable research avenues:

1) Image generation: Crafting realistic images from random noise showcases the creative potential of GANs, with applications spanning art, design, and data augmentation.

2) Style transfer: Enabling the transfer of artistic styles between images, merging creativity and technology to yield visually captivating results.

3) Anomaly detection: GANs find utility in identifying anomalies within datasets, bolstering fraud detection, quality control, and anomaly-sensitive industries.

Robotics and AI

The synergy between Robotics and AI is a fertile ground for exploration, with Artificial Intelligence Topics such as:

1) Human-robot collaboration: Research in this arena strives to establish harmonious collaboration between humans and robots, augmenting industry productivity and efficiency.

2) Robot learning: By enabling robots to learn and adapt from their experiences, Researchers foster robots' autonomy and the ability to handle diverse tasks.

3) Ethical considerations: Delving into the ethical implications surrounding AI-powered robots helps establish responsible guidelines for their deployment.

AI in healthcare

AI presents a transformative potential within healthcare, spurring research into:

1) Medical diagnosis: AI aids in accurately diagnosing medical conditions, revolutionising early detection and patient care.

2) Drug discovery: Leveraging AI for drug discovery expedites the identification of potential candidates, accelerating the development of new treatments.

3) Personalised treatment: Tailoring medical interventions to individual patient profiles enhances treatment outcomes and patient well-being.

AI for social good

Harnessing the prowess of AI for Social Good entails addressing pressing global challenges:

1) Environmental monitoring: AI-powered solutions facilitate real-time monitoring of ecological changes, supporting conservation and sustainable practices.

2) Disaster response: Research in this area bolsters disaster response efforts by employing AI to analyse data and optimise resource allocation.

3) Poverty alleviation: Researchers contribute to humanitarian efforts and socioeconomic equality by devising AI solutions to tackle poverty.

Unlock the potential of Artificial Intelligence for effective Project Management with our Artificial Intelligence (AI) for Project Managers Course . Sign up now!

Autonomous vehicles

Autonomous Vehicles represent a realm brimming with potential and complexities, necessitating research in Artificial Intelligence Topics such as:

1) Sensor fusion: Integrating data from diverse sensors enhances perception accuracy, which is essential for safe autonomous navigation.

2) Path planning: Developing advanced algorithms for path planning ensures optimal routes while adhering to safety protocols.

3) Safety and ethics: Ethical considerations, such as programming vehicles to make difficult decisions in potential accident scenarios, require meticulous research and deliberation.

AI ethics and bias

Ethical underpinnings in AI drive research efforts in these directions:

1) Fairness in AI: Ensuring AI systems remain impartial and unbiased across diverse demographic groups.

2) Bias detection and mitigation: Identifying and rectifying biases present within AI models guarantees equitable outcomes.

3) Ethical decision-making: Developing frameworks that imbue AI with ethical decision-making capabilities aligns technology with societal values.

Future of AI

The vanguard of AI beckons Researchers to explore these horizons:

1) Artificial General Intelligence (AGI): Speculating on the potential emergence of AI systems capable of emulating human-like intelligence opens dialogues on the implications and challenges.

2) AI and creativity: Probing the interface between AI and creative domains, such as art and music, unveils the coalescence of human ingenuity and technological prowess.

3) Ethical and regulatory challenges: Researching the ethical dilemmas and regulatory frameworks underpinning AI's evolution fortifies responsible innovation.

AI and education

The intersection of AI and Education opens doors to innovative learning paradigms:

1) Personalised learning: Developing AI systems that adapt educational content to individual learning styles and paces.

2) Intelligent tutoring systems: Creating AI-driven tutoring systems that provide targeted support to students.

3) Educational data mining: Applying AI to analyse educational data for insights into learning patterns and trends.

Unleash the full potential of AI with our comprehensive Introduction to Artificial Intelligence Training . Join now!

Conclusion

The domain of AI is ever-expanding, rich with intriguing topics about Artificial Intelligence that beckon Researchers to explore, question, and innovate. Through the pursuit of these twelve diverse Artificial Intelligence Topics, we pave the way for not only technological advancement but also a deeper understanding of the societal impact of AI. By delving into these realms, Researchers stand poised to shape the trajectory of AI, ensuring it remains a force for progress, empowerment, and positive transformation in our world.

Unlock your full potential with our extensive Personal Development Training Courses. Join today!

Frequently Asked Questions

Upcoming data, analytics & ai resources batches & dates.

Fri 26th Apr 2024

Fri 2nd Aug 2024

Fri 15th Nov 2024

Get A Quote

WHO WILL BE FUNDING THE COURSE?

My employer

By submitting your details you agree to be contacted in order to respond to your enquiry

Business Analysis
Lean Six Sigma Certification

Share this course

Our biggest spring sale.

We cannot process your enquiry without contacting you, please tick to confirm your consent to us for contacting you about your enquiry.

By submitting your details you agree to be contacted in order to respond to your enquiry.

We may not have the course you’re looking for. If you enquire or give us a call on 01344203999 and speak to our training experts, we may still be able to help with your training requirements.

Or select from our popular topics

ITIL® Certification
Scrum Certification
Change Management Certification
Business Analysis Courses
Microsoft Azure Certification
Microsoft Excel Courses
Microsoft Project
Explore more courses

Press esc to close

Fill out your contact details below and our training experts will be in touch.

Fill out your contact details below

Thank you for your enquiry!

One of our training experts will be in touch shortly to go over your training requirements.

Back to Course Information

Fill out your contact details below so we can get in touch with you regarding your training requirements.

* WHO WILL BE FUNDING THE COURSE?

Preferred Contact Method

No preference

Back to course information

Fill out your training details below

Fill out your training details below so we have a better idea of what your training requirements are.

HOW MANY DELEGATES NEED TRAINING?

HOW DO YOU WANT THE COURSE DELIVERED?

Online Instructor-led

Online Self-paced

WHEN WOULD YOU LIKE TO TAKE THIS COURSE?

Next 2 - 4 months

WHAT IS YOUR REASON FOR ENQUIRING?

Looking for some information

Looking for a discount

I want to book but have questions

One of our training experts will be in touch shortly to go overy your training requirements.

Your privacy & cookies!

Like many websites we use cookies. We care about your data and experience, so to give you the best possible experience using our site, we store a very limited amount of your data. Continuing to use this site or clicking “Accept & close” means that you agree to our use of cookies. Learn more about our privacy policy and cookie policy cookie policy .

We use cookies that are essential for our site to work. Please visit our cookie policy for more information. To accept all cookies click 'Accept & close'.

Python Basics
Interview Questions
Python Quiz
Popular Packages
Python Projects
Practice Python
AI With Python
Learn Python3
Python Automation
Python Web Dev
DSA with Python
Python OOPs
Dictionaries
How Amazon Uses Machine Learning?
Artificial Intelligence Could be a Better Doctor
10 Most Interesting Chatbots in the World
What Are DeepFakes And How Dangerous Are They?
Impact of AI and ML On Warfare Techniques
What is the Role of Artificial Intelligence in Fighting Coronavirus?
Top 7 Artificial Intelligence and Machine Learning Trends For 2022
5 Algorithms that Demonstrate Artificial Intelligence Bias
What is Artificial Intelligence as a Service (AIaaS) in the Tech Industry?
Top 10 Business Intelligence Platforms in 2020
What is IBM Watson and Its Services?
8 Best Artificial Intelligence Books For Beginners in 2024
Can Artificial Intelligence Help in Curing Cancer?
Is AI Really a Threat to Cybersecurity?
10 Best Artificial Intelligence Project Ideas To Kick-Start Your Career
5 Best Humanoid Robots in The World
Propositional Logic based Agent
Resolution Algorithm in Artificial Intelligence
Face recognition using Artificial Intelligence

8 Best Topics for Research and Thesis in Artificial Intelligence

Imagine a future in which intelligence is not restricted to humans!!! A future where machines can think as well as humans and work with them to create an even more exciting universe. While this future is still far away, Artificial Intelligence has still made a lot of advancement in these times. There is a lot of research being conducted in almost all fields of AI like Quantum Computing, Healthcare, Autonomous Vehicles, Internet of Things , Robotics , etc. So much so that there is an increase of 90% in the number of annually published research papers on Artificial Intelligence since 1996. Keeping this in mind, if you want to research and write a thesis based on Artificial Intelligence, there are many sub-topics that you can focus on. Some of these topics along with a brief introduction are provided in this article. We have also mentioned some published research papers related to each of these topics so that you can better understand the research process.

So without further ado, let’s see the different Topics for Research and Thesis in Artificial Intelligence!

1. Machine Learning

Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!!!) This process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate. However, generally speaking, Machine Learning Algorithms are divided into 3 types i.e. Supervised Machine Learning Algorithms, Unsupervised Machine Learning Algorithms , and Reinforcement Machine Learning Algorithms.

2. Deep Learning

Deep Learning is a subset of Machine Learning that learns by imitating the inner working of the human brain in order to process data and implement decisions based on that data. Basically, Deep Learning uses artificial neural networks to implement machine learning. These neural networks are connected in a web-like structure like the networks in the human brain (Basically a simplified version of our brain!). This web-like structure of artificial neural networks means that they are able to process data in a nonlinear approach which is a significant advantage over traditional algorithms that can only process data in a linear approach. An example of a deep neural network is RankBrain which is one of the factors in the Google Search algorithm.

3. Reinforcement Learning

Reinforcement Learning is a part of Artificial Intelligence in which the machine learns something in a way that is similar to how humans learn. As an example, assume that the machine is a student. Here the hypothetical student learns from its own mistakes over time (like we had to!!). So the Reinforcement Machine Learning Algorithms learn optimal actions through trial and error. This means that the algorithm decides the next action by learning behaviors that are based on its current state and that will maximize the reward in the future. And like humans, this works for machines as well! For example, Google’s AlphaGo computer program was able to beat the world champion in the game of Go (that’s a human!) in 2017 using Reinforcement Learning.

4. Robotics

Robotics is a field that deals with creating humanoid machines that can behave like humans and perform some actions like human beings. Now, robots can act like humans in certain situations but can they think like humans as well? This is where artificial intelligence comes in! AI allows robots to act intelligently in certain situations. These robots may be able to solve problems in a limited sphere or even learn in controlled environments. An example of this is Kismet , which is a social interaction robot developed at M.I.T’s Artificial Intelligence Lab. It recognizes the human body language and also our voice and interacts with humans accordingly. Another example is Robonaut , which was developed by NASA to work alongside the astronauts in space.

5. Natural Language Processing

It’s obvious that humans can converse with each other using speech but now machines can too! This is known as Natural Language Processing where machines analyze and understand language and speech as it is spoken (Now if you talk to a machine it may just talk back!). There are many subparts of NLP that deal with language such as speech recognition, natural language generation, natural language translation , etc. NLP is currently extremely popular for customer support applications, particularly the chatbot . These chatbots use ML and NLP to interact with the users in textual form and solve their queries. So you get the human touch in your customer support interactions without ever directly interacting with a human.

Some Research Papers published in the field of Natural Language Processing are provided here. You can study them to get more ideas about research and thesis on this topic.

6. Computer Vision

The internet is full of images! This is the selfie age, where taking an image and sharing it has never been easier. In fact, millions of images are uploaded and viewed every day on the internet. To make the most use of this huge amount of images online, it’s important that computers can see and understand images. And while humans can do this easily without a thought, it’s not so easy for computers! This is where Computer Vision comes in. Computer Vision uses Artificial Intelligence to extract information from images. This information can be object detection in the image, identification of image content to group various images together, etc. An application of computer vision is navigation for autonomous vehicles by analyzing images of surroundings such as AutoNav used in the Spirit and Opportunity rovers which landed on Mars.

7. Recommender Systems

When you are using Netflix, do you get a recommendation of movies and series based on your past choices or genres you like? This is done by Recommender Systems that provide you some guidance on what to choose next among the vast choices available online. A Recommender System can be based on Content-based Recommendation or even Collaborative Filtering. Content-Based Recommendation is done by analyzing the content of all the items. For example, you can be recommended books you might like based on Natural Language Processing done on the books. On the other hand, Collaborative Filtering is done by analyzing your past reading behavior and then recommending books based on that.

8. Internet of Things

Artificial Intelligence deals with the creation of systems that can learn to emulate human tasks using their prior experience and without any manual intervention. Internet of Things , on the other hand, is a network of various devices that are connected over the internet and they can collect and exchange data with each other. Now, all these IoT devices generate a lot of data that needs to be collected and mined for actionable results. This is where Artificial Intelligence comes into the picture. Internet of Things is used to collect and handle the huge amount of data that is required by the Artificial Intelligence algorithms. In turn, these algorithms convert the data into useful actionable results that can be implemented by the IoT devices.

Please Login to comment...

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

How to Write a Better Thesis Statement Using AI (2023 Updated)

Meredith Sell

With the exceptions of poetry and fiction, every piece of writing needs a thesis statement.

- Opinion pieces for the local newspaper? Yes.

- An essay for a college class? You betcha.

- A book about China’s Ming Dynasty? Absolutely.

All of these pieces of writing need a thesis statement that sums up what they’re about and tells the reader what to expect, whether you’re making an argument, describing something in detail, or exploring ideas.

But how do you write a thesis statement? How do you even come up with one?

This step-by-step guide will show you exactly how — and help you make sure every thesis statement you write has all the parts needed to be clear, coherent, and complete.

Let’s start by making sure we understand what a thesis is (and what it’s not).

What Is a Thesis Statement?

A thesis statement is a one or two sentence long statement that concisely describes your paper’s subject, angle or position — and offers a preview of the evidence or argument your essay will present.

A thesis is not:

An exclamation
A simple fact

Think of your thesis as the road map for your essay. It briefly charts where you’ll start (subject), what you’ll cover (evidence/argument), and where you’ll land (position, angle).

Writing a thesis early in your essay writing process can help you keep your writing focused, so you won’t get off-track describing something that has nothing to do with your central point. Your central point is your thesis, and the rest of your essay fleshes it out.

Get help writing your thesis statement with this FREE AI tool > Get help writing your thesis statement with this FREE AI tool >

Different Kinds of Papers Need Different Kinds of Theses

How you compose your thesis will depend on the type of essay you’re writing. For academic writing, there are three main kinds of essays:

Persuasive, aka argumentative
Expository, aka explanatory

A persuasive essay requires a thesis that clearly states the central stance of the paper , what the rest of the paper will argue in support of.

Paper books are superior to ebooks when it comes to form, function, and overall reader experience.

An expository essay’s thesis sets up the paper’s focus and angle — the paper’s unique take, what in particular it will be describing and why . The why element gives the reader a reason to read; it tells the reader why the topic matters.

Understanding the functional design of physical books can help ebook designers create digital reading experiences that usher readers into literary worlds without technological difficulties.

A narrative essay is similar to that of an expository essay, but it may be less focused on tangible realities and more on intangibles of, for example, the human experience.

The books I’ve read over the years have shaped me, opening me up to worlds and ideas and ways of being that I would otherwise know nothing about.

As you prepare to craft your thesis, think through the goal of your paper. Are you making an argument? Describing the chemical properties of hydrogen? Exploring your relationship with the outdoors? What do you want the reader to take away from reading your piece?

Make note of your paper’s goal and then walk through our thesis-writing process.

Now that you practically have a PhD in theses, let’s learn how to write one:

How to Write (and Develop) a Strong Thesis

If developing a thesis is stressing you out, take heart — basically no one has a strong thesis right away. Developing a thesis is a multi-step process that takes time, thought, and perhaps most important of all: research .

Tackle these steps one by one and you’ll soon have a thesis that’s rock-solid.

1. Identify your essay topic.

Are you writing about gardening? Sword etiquette? King Louis XIV?

With your assignment requirements in mind, pick out a topic (or two) and do some preliminary research . Read up on the basic facts of your topic. Identify a particular angle or focus that’s interesting to you. If you’re writing a persuasive essay, look for an aspect that people have contentious opinions on (and read our piece on persuasive essays to craft a compelling argument).

If your professor assigned a particular topic, you’ll still want to do some reading to make sure you know enough about the topic to pick your specific angle.

For those writing narrative essays involving personal experiences, you may need to do a combination of research and freewriting to explore the topic before honing in on what’s most compelling to you.

Once you have a clear idea of the topic and what interests you, go on to the next step.

2. Ask a research question.

You know what you’re going to write about, at least broadly. Now you just have to narrow in on an angle or focus appropriate to the length of your assignment. To do this, start by asking a question that probes deeper into your topic.

This question may explore connections between causes and effects, the accuracy of an assumption you have, or a value judgment you’d like to investigate, among others.

For example, if you want to write about gardening for a persuasive essay and you’re interested in raised garden beds, your question could be:

What are the unique benefits of gardening in raised beds versus on the ground? Is one better than the other?

Or if you’re writing about sword etiquette for an expository essay , you could ask:

How did sword etiquette in Europe compare to samurai sword etiquette in Japan?

How does medieval sword etiquette influence modern fencing?

Kickstart your curiosity and come up with a handful of intriguing questions. Then pick the two most compelling to initially research (you’ll discard one later).

3. Answer the question tentatively.

You probably have an initial thought of what the answer to your research question is. Write that down in as specific terms as possible. This is your working thesis .

Gardening in raised beds is preferable because you won’t accidentally awaken dormant weed seeds — and you can provide more fertile soil and protection from invasive species.

Medieval sword-fighting rituals are echoed in modern fencing etiquette.

Why is a working thesis helpful?

Both your research question and your working thesis will guide your research. It’s easy to start reading anything and everything related to your broad topic — but for a 4-, 10-, or even 20-page paper, you don’t need to know everything. You just need the relevant facts and enough context to accurately and clearly communicate to your reader.

Your working thesis will not be identical to your final thesis, because you don’t know that much just yet.

This brings us to our next step:

4. Research the question (and working thesis).

What do you need to find out in order to evaluate the strength of your thesis? What do you need to investigate to answer your research question more fully?

Comb through authoritative, trustworthy sources to find that information. And keep detailed notes.

As you research, evaluate the strengths and weaknesses of your thesis — and see what other opposing or more nuanced theses exist.

If you’re writing a persuasive essay, it may be helpful to organize information according to what does or does not support your thesis — or simply gather the information and see if it’s changing your mind. What new opinion do you have now that you’ve learned more about your topic and question? What discoveries have you made that discredit or support your initial thesis?

Raised garden beds prevent full maturity in certain plants — and are more prone to cold, heat, and drought.

If you’re writing an expository essay, use this research process to see if your initial idea holds up to the facts. And be on the lookout for other angles that would be more appropriate or interesting for your assignment.

Modern fencing doesn’t share many rituals with medieval swordplay.

With all this research under your belt, you can answer your research question in-depth — and you’ll have a clearer idea of whether or not your working thesis is anywhere near being accurate or arguable. What’s next?

5. Refine your thesis.

If you found that your working thesis was totally off-base, you’ll probably have to write a new one from scratch.

For a persuasive essay , maybe you found a different opinion far more compelling than your initial take. For an expository essay , maybe your initial assumption was completely wrong — could you flip your thesis around and inform your readers of what you learned?

Use what you’ve learned to rewrite or revise your thesis to be more accurate, specific, and compelling.

Raised garden beds appeal to many gardeners for the semblance of control they offer over what will and will not grow, but they are also more prone to changes in weather and air temperature and may prevent certain plants from reaching full maturity. All of this makes raised beds the worse option for ambitious gardeners.

While swordplay can be traced back through millennia, modern fencing has little in common with medieval combat where swordsmen fought to the death.

If you’ve been researching two separate questions and theses, now’s the time to evaluate which one is most interesting, compelling, or appropriate for your assignment. Did one thesis completely fall apart when faced with the facts? Did one fail to turn up any legitimate sources or studies? Choose the stronger question or the more interesting (revised) thesis, and discard the other.

6. Get help from AI

To make the process even easier, you can take advantage of Wordtune's generative AI capabilities to craft an effective thesis statement. You can take your current thesis statement and try the paraphrase tool to get suggestions for better ways of articulating it. WordTune will generate a set of related phrases, which you can select to help you refine your statement. You can also use Wordtune's suggestions to craft the thesis statement. Write your initial introduction sentence, then click '+' and select the explain suggestion. Browse through the suggestions until you have a statement that captures your idea perfectly.

Thesis Check: Look for These Three Elements

At this point, you should have a thesis that will set up an original, compelling essay, but before you set out to write that essay, make sure your thesis contains these three elements:

Topic: Your thesis should clearly state the topic of your essay, whether swashbuckling pirates, raised garden beds, or methods of snow removal.
Position or angle: Your thesis should zoom into the specific aspect of your topic that your essay will focus on, and briefly but boldly state your position or describe your angle.
Summary of evidence and/or argument: In a concise phrase or two, your thesis should summarize the evidence and/or argument your essay will present, setting up your readers for what’s coming without giving everything away.

The challenge for you is communicating each of these elements in a sentence or two. But remember: Your thesis will come at the end of your intro, which will already have done some work to establish your topic and focus. Those aspects don’t need to be over explained in your thesis — just clearly mentioned and tied to your position and evidence.

Let’s look at our examples from earlier to see how they accomplish this:

Notice how:

The topic is mentioned by name.
The position or angle is clearly stated.
The evidence or argument is set up, as well as the assumptions or opposing view that the essay will debunk.

Both theses prepare the reader for what’s coming in the rest of the essay:

An argument to show that raised beds are actually a poor option for gardeners who want to grow thriving, healthy, resilient plants.
An exposition of modern fencing in comparison with medieval sword fighting that shows how different they are.

Examine your refined thesis. Are all three elements present? If any are missing, make any additions or clarifications needed to correct it.

It’s Essay-Writing Time!

Now that your thesis is ready to go, you have the rest of your essay to think about. With the work you’ve already done to develop your thesis, you should have an idea of what comes next — but if you need help forming your persuasive essay’s argument, we’ve got a blog for that.

Share This Article:

Finding the OG Writing Assistant – Wordtune vs. QuillBot

What’s a Split Infinitive? Definition + When to Avoid It

Preparing for Graduate School: 8 Tips to Know

Looking for fresh content, thank you your submission has been received.

Artificial Intelligence Thesis Statement

An artificial intelligence thesis statement is a statement of intent that outlines the purpose and direction of research into artificial intelligence. It provides a framework for the research and is used to guide the project throughout its development. In this statement, the researcher outlines the questions they will be addressing and the methods they plan to use to answer those questions. The thesis statement also serves as a tool to help focus the research project and provide clarity for the researcher and the readers.

Challenges of Developing an AI Thesis Statement

Developing an AI thesis statement is a daunting task for many students as it requires a great deal of research and a thorough understanding of the subject. It is a complex process that requires a lot of thought, planning, and effort. AI thesis statements are often very specific and require a lot of technical knowledge in order to be effective. It is important to consider the audience of the statement, the type of research being conducted, and the purpose of the statement before beginning the process.

It is essential that an AI thesis statement be clear and concise, as it serves as the foundation for the entire paper. It should be able to capture the reader’s attention and explain the main points of the paper in a few sentences. In addition, the thesis statement must be well-structured and organized, as this will help readers better understand the paper’s main points. Furthermore, the statement should be backed by evidence, as this will help support the main arguments of the paper.

Finally, an AI thesis statement should be able to effectively convey the paper’s main ideas in a concise manner. It should be able to concisely explain the main points of the paper and provide a clear direction for the paper. This will help readers understand the main points of the paper and allow them to easily follow the paper’s argument.

Benefits of an AI Thesis Statement

The world is rapidly changing due to the emergence of artificial intelligence (AI). AI is transforming the way businesses operate, and is becoming an integral part of many aspects of our lives. An AI thesis statement can be a powerful tool for students and researchers in the field of AI, allowing them to explore the many possibilities of AI and its implications.

An AI thesis statement should include the main components of a research paper. This includes an introduction, a problem statement, research objectives, and a conclusion. It should also include the relevant data, analysis, and evidence to support the thesis. The thesis statement should also discuss the implications of the research findings, and the impact of AI on society.

AI thesis statements are also beneficial to students in terms of understanding the implications of their research. They can help students consider the various ethical implications of their research, and the current state of AI in the world. AI thesis statements can also provide students with a better understanding of the challenges and opportunities that AI presents to society.

In conclusion, an AI thesis statement can provide students and researchers with a valuable tool to explore the implications of AI. It can help them to consider the ethical implications of their research, and the current state of AI in the world. It can also provide students with a better understanding of the challenges and opportunities that AI presents to society.

Types of AI Thesis Statements

Artificial Intelligence (AI) is a rapidly growing field of research and development. With its potential to revolutionize the world, AI has become a hot topic of conversation in many sectors. One way to approach this subject matter is by writing a thesis statement. A thesis statement helps to focus the direction of research and development and to give readers an understanding of the core argument. There are several types of AI thesis statements that can be used for different topics.

The first type of thesis statement is a general thesis statement. This type of statement provides an overview of the research, and it should be succinct and clear. It should also provide a succinct argument that can be supported with evidence. Another type of thesis statement is a research thesis. This type of statement is designed to explore a specific area of AI and provide a detailed analysis of the research conducted.

The third type of AI thesis statement is a position thesis statement. This statement takes a stance on a particular issue or topic, and it should be written with strong evidence to support the argument. Finally, a theoretical thesis statement can be used to express an idea or opinion that is based on the research conducted and is not necessarily supported by the evidence.

No matter what type of AI thesis statement is being written, it is important to ensure that it is concise, clear, and well-argued. It should also provide an in-depth analysis of the topic and be SEO friendly, informative, and optimized for search engines. By following these tips, writers can effectively craft an AI thesis statement that is engaging and unique.

How to Create an AI Thesis Statement

Creating an AI thesis statement can be a daunting task, but it is essential in order to write an effective, compelling paper on the subject. A strong thesis statement should capture the essence of your argument and be specific enough to provide direction for the paper. Here are some tips for crafting a well-crafted AI thesis statement.

First, consider the scope of the argument you are making. What are the key points you are trying to make? Determine an argument, then narrow down the scope so that the statement is focused and clear. This will help ensure that your paper is not overly broad or too vague.

Second, consider the audience you are writing for. The thesis statement should be tailored to the audience’s needs and expectations. Use language that is appropriate for the level of expertise of the readers and make sure you use terms that are familiar to them.

Third, be sure to include evidence to support your points. Evidence can come from sources such as statistics, scholarly articles, and interviews. Make sure you choose reliable and relevant sources and cite them properly.

Finally, ensure that your thesis statement is concise and clear. Avoid using overly complex language and lengthy sentences. Keep it simple and direct so that readers can quickly and easily understand your argument.

By following these steps, you can craft an AI thesis statement that is clear, concise, and effective. With the right approach, you can create a compelling argument that will keep your readers engaged and will help to ensure that your paper meets its objectives.

Researching AI Thesis Statements

Artificial Intelligence (AI) is an ever-evolving field of technology that promises to revolutionize the way we live and work. With its potential to transform the world, it is no wonder that many students are looking into writing a thesis or dissertation on this topic. But before you can start writing, you must first craft a compelling thesis statement. The thesis statement is the backbone of your research, so it is important to make sure that it is well-crafted and thoughtfully considered.

A great starting point for researching an AI thesis statement is to look at recent studies and advancements in the field. This will give you a better understanding of the current state of AI and help you to hone in on the specific elements of the technology that you want to focus on. Additionally, it can serve as a source of inspiration for your thesis statement.

You can also draw inspiration from the experts in the field. AI professionals and academics have a wealth of knowledge and experience that can help to guide your research. By consulting the experts, you can gain a better understanding of the field and the potential applications of AI technology.

Finally, you should also consider the impact that AI could have on society. This is an important factor to consider when crafting your thesis statement, as it will help to illustrate the importance of your research to the wider world.

By researching current studies, consulting AI experts, and considering the potential impact of the technology, you can craft an AI thesis statement that is sure to impress and captivate your readers. With some time and effort, you can create a strong and compelling thesis statement that will serve as the foundation of your research.

Examples of AI Thesis Statements

Artificial Intelligence (AI) is a rapidly growing field of technology, with new advancements being made every day. AI is being used in a variety of industries, from healthcare to finance, making it an incredibly important topic to discuss. As AI continues to evolve, so does the need to delve into the complexities of the technology and analyze its implications. Writing an AI thesis statement can be a daunting task, but it doesn’t have to be. By exploring examples of AI thesis statements, you can develop a well-crafted and thought-provoking thesis of your own.

A well-written thesis statement should clearly state the main point of the paper and provide a roadmap for the rest of the paper. A thesis statement on AI could include a statement about the importance of AI, the potential of AI, or the implications of AI for the future of society. For example, a thesis statement on AI could be, “AI will revolutionize the way we work, learn, and interact with the world in the future.”

Other examples of AI thesis statements could focus on specific technologies or ideas. For example, a thesis statement on machine learning could be, “Machine learning is transforming the way we access and process data, and will have a profound impact on the way we live and work in the future.”

When writing an AI thesis statement, it is important to think critically about the implications of AI, as well as the potential applications of the technology. Additionally, you should be sure to consider the ethical implications of AI, such as privacy and security concerns. By taking the time to explore these topics, you can develop a thesis statement that is not only informative, but also thought-provoking.

FAQs About the Artificial Intelligence Thesis Statement

Q1. What should I include in my artificial intelligence thesis statement?

A1. Your thesis statement should outline the main argument of your paper and provide a brief overview of the points you will use to support your argument. Additionally, you should include any definitions and background information necessary to understand your argument.

Q2. How do I make sure my artificial intelligence thesis statement is effective?

A2. Your thesis statement should be clear and concise. Additionally, it should provide a strong argument that can be supported with evidence. A good way to check the effectiveness of your statement is to ask yourself whether it can be argued against.

Q3. What are some common topics for an artificial intelligence thesis statement?

A3. Common topics for an artificial intelligence thesis statement include the use of AI in healthcare, the ethics of using AI in decision-making, the economic effects of AI, and the impact of AI on employment.

In conclusion, Artificial Intelligence (AI) has the potential to revolutionize many industries and has the power to fundamentally change the way we live and work. AI technology can be used to automate processes, reduce costs, improve efficiency, and even help to make decisions that would otherwise be beyond human capabilities. A well-crafted thesis statement on the subject of AI can help to provide a launching point for further research and discussion.

Who Leads In Artificial Intelligence

Why Study Artificial Intelligence

Which Software Is Used To Make Artificial Intelligence

Completed Theses

State space search solves navigation tasks and many other real world problems. Heuristic search, especially greedy best-first search, is one of the most successful algorithms for state space search. We improve the state of the art in heuristic search in three directions.

In Part I, we present methods to train neural networks as powerful heuristics for a given state space. We present a universal approach to generate training data using random walks from a (partial) state. We demonstrate that our heuristics trained for a specific task are often better than heuristics trained for a whole domain. We show that the performance of all trained heuristics is highly complementary. There is no clear pattern, which trained heuristic to prefer for a specific task. In general, model-based planners still outperform planners with trained heuristics. But our approaches exceed the model-based algorithms in the Storage domain. To our knowledge, only once before in the Spanner domain, a learning-based planner exceeded the state-of-the-art model-based planners.

A priori, it is unknown whether a heuristic, or in the more general case a planner, performs well on a task. Hence, we trained online portfolios to select the best planner for a task. Today, all online portfolios are based on handcrafted features. In Part II, we present new online portfolios based on neural networks, which receive the complete task as input, and not just a few handcrafted features. Additionally, our portfolios can reconsider their choices. Both extensions greatly improve the state-of-the-art of online portfolios. Finally, we show that explainable machine learning techniques, as the alternative to neural networks, are also good online portfolios. Additionally, we present methods to improve our trust in their predictions.

Even if we select the best search algorithm, we cannot solve some tasks in reasonable time. We can speed up the search if we know how it behaves in the future. In Part III, we inspect the behavior of greedy best-first search with a fixed heuristic on simple tasks of a domain to learn its behavior for any task of the same domain. Once greedy best-first search expanded a progress state, it expands only states with lower heuristic values. We learn to identify progress states and present two methods to exploit this knowledge. Building upon this, we extract the bench transition system of a task and generalize it in such a way that we can apply it to any task of the same domain. We can use this generalized bench transition system to split a task into a sequence of simpler searches.

In all three research directions, we contribute new approaches and insights to the state of the art, and we indicate interesting topics for future work.

Greedy best-first search (GBFS) is a sibling of A* in the family of best-first state-space search algorithms. While A* is guaranteed to find optimal solutions of search problems, GBFS does not provide any guarantees but typically finds satisficing solutions more quickly than A*. A classical result of optimal best-first search shows that A* with admissible and consistent heuristic expands every state whose f-value is below the optimal solution cost and no state whose f-value is above the optimal solution cost. Theoretical results of this kind are useful for the analysis of heuristics in different search domains and for the improvement of algorithms. For satisficing algorithms a similarly clear understanding is currently lacking. We examine the search behavior of GBFS in order to make progress towards such an understanding.

We introduce the concept of high-water mark benches, which separate the search space into areas that are searched by GBFS in sequence. High-water mark benches allow us to exactly determine the set of states that GBFS expands under at least one tie-breaking strategy. We show that benches contain craters. Once GBFS enters a crater, it has to expand every state in the crater before being able to escape.

Benches and craters allow us to characterize the best-case and worst-case behavior of GBFS in given search instances. We show that computing the best-case or worst-case behavior of GBFS is NP-complete in general but can be computed in polynomial time for undirected state spaces.

We present algorithms for extracting the set of states that GBFS potentially expands and for computing the best-case and worst-case behavior. We use the algorithms to analyze GBFS on benchmark tasks from planning competitions under a state-of-the-art heuristic. Experimental results reveal interesting characteristics of the heuristic on the given tasks and demonstrate the importance of tie-breaking in GBFS.

Classical planning tackles the problem of finding a sequence of actions that leads from an initial state to a goal. Over the last decades, planning systems have become significantly better at answering the question whether such a sequence exists by applying a variety of techniques which have become more and more complex. As a result, it has become nearly impossible to formally analyze whether a planning system is actually correct in its answers, and we need to rely on experimental evidence.

One way to increase trust is the concept of certifying algorithms, which provide a witness which justifies their answer and can be verified independently. When a planning system finds a solution to a problem, the solution itself is a witness, and we can verify it by simply applying it. But what if the planning system claims the task is unsolvable? So far there was no principled way of verifying this claim.

This thesis contributes two approaches to create witnesses for unsolvable planning tasks. Inductive certificates are based on the idea of invariants. They argue that the initial state is part of a set of states that we cannot leave and that contains no goal state. In our second approach, we define a proof system that proves in an incremental fashion that certain states cannot be part of a solution until it has proven that either the initial state or all goal states are such states.

Both approaches are complete in the sense that a witness exists for every unsolvable planning task, and can be verified efficiently (in respect to the size of the witness) by an independent verifier if certain criteria are met. To show their applicability to state-of-the-art planning techniques, we provide an extensive overview how these approaches can cover several search algorithms, heuristics and other techniques. Finally, we show with an experimental study that generating and verifying these explanations is not only theoretically possible but also practically feasible, thus making a first step towards fully certifying planning systems.

Heuristic search with an admissible heuristic is one of the most prominent approaches to solving classical planning tasks optimally. In the first part of this thesis, we introduce a new family of admissible heuristics for classical planning, based on Cartesian abstractions, which we derive by counterexample-guided abstraction refinement. Since one abstraction usually is not informative enough for challenging planning tasks, we present several ways of creating diverse abstractions. To combine them admissibly, we introduce a new cost partitioning algorithm, which we call saturated cost partitioning. It considers the heuristics sequentially and uses the minimum amount of costs that preserves all heuristic estimates for the current heuristic before passing the remaining costs to subsequent heuristics until all heuristics have been served this way.

In the second part, we show that saturated cost partitioning is strongly influenced by the order in which it considers the heuristics. To find good orders, we present a greedy algorithm for creating an initial order and a hill-climbing search for optimizing a given order. Both algorithms make the resulting heuristics significantly more accurate. However, we obtain the strongest heuristics by maximizing over saturated cost partitioning heuristics computed for multiple orders, especially if we actively search for diverse orders.

The third part provides a theoretical and experimental comparison of saturated cost partitioning and other cost partitioning algorithms. Theoretically, we show that saturated cost partitioning dominates greedy zero-one cost partitioning. The difference between the two algorithms is that saturated cost partitioning opportunistically reuses unconsumed costs for subsequent heuristics. By applying this idea to uniform cost partitioning we obtain an opportunistic variant that dominates the original. We also prove that the maximum over suitable greedy zero-one cost partitioning heuristics dominates the canonical heuristic and show several non-dominance results for cost partitioning algorithms. The experimental analysis shows that saturated cost partitioning is the cost partitioning algorithm of choice in all evaluated settings and it even outperforms the previous state of the art in optimal classical planning.

Classical planning is the problem of finding a sequence of deterministic actions in a state space that lead from an initial state to a state satisfying some goal condition. The dominant approach to optimally solve planning tasks is heuristic search, in particular A* search combined with an admissible heuristic. While there exist many different admissible heuristics, we focus on abstraction heuristics in this thesis, and in particular, on the well-established merge-and-shrink heuristics.

Our main theoretical contribution is to provide a comprehensive description of the merge-and-shrink framework in terms of transformations of transition systems. Unlike previous accounts, our description is fully compositional, i.e. can be understood by understanding each transformation in isolation. In particular, in addition to the name-giving merge and shrink transformations, we also describe pruning and label reduction as such transformations. The latter is based on generalized label reduction, a new theory that removes all of the restrictions of the previous definition of label reduction. We study the four types of transformations in terms of desirable formal properties and explain how these properties transfer to heuristics being admissible and consistent or even perfect. We also describe an optimized implementation of the merge-and-shrink framework that substantially improves the efficiency compared to previous implementations.

Furthermore, we investigate the expressive power of merge-and-shrink abstractions by analyzing factored mappings, the data structure they use for representing functions. In particular, we show that there exist certain families of functions that can be compactly represented by so-called non-linear factored mappings but not by linear ones.

On the practical side, we contribute several non-linear merge strategies to the merge-and-shrink toolbox. In particular, we adapt a merge strategy from model checking to planning, provide a framework to enhance existing merge strategies based on symmetries, devise a simple score-based merge strategy that minimizes the maximum size of transition systems of the merge-and-shrink computation, and describe another framework to enhance merge strategies based on an analysis of causal dependencies of the planning task.

In a large experimental study, we show the evolution of the performance of merge-and-shrink heuristics on planning benchmarks. Starting with the state of the art before the contributions of this thesis, we subsequently evaluate all of our techniques and show that state-of-the-art non-linear merge-and-shrink heuristics improve significantly over the previous state of the art.

Admissible heuristics are the main ingredient when solving classical planning tasks optimally with heuristic search. Higher admissible heuristic values are more accurate, so combining them in a way that dominates their maximum and remains admissible is an important problem.

The thesis makes three contributions in this area. Extensions to cost partitioning (a well-known heuristic combination framework) allow to produce higher estimates from the same set of heuristics. The new heuristic family called operator-counting heuristics unifies many existing heuristics and offers a new way to combine them. Another new family of heuristics called potential heuristics allows to cast the problem of finding a good heuristic as an optimization problem.

Both operator-counting and potential heuristics are closely related to cost partitioning. They offer a new look on cost partitioned heuristics and already sparked research beyond their use as classical planning heuristics.

Master's theses

Classical planning tasks are typically formulated in PDDL. Some of them can be described more concisely using derived variables. Contrary to basic variables, their values cannot be changed by operators and are instead determined by axioms which specify conditions under which they take a certain value. Planning systems often support axioms in their search component, but their heuristics’ support is limited or nonexistent. This leads to decreased search performance with tasks that use axioms. We compile axioms away using our implementation of a known algorithm in the Fast Downward planner. Our results show that the compilation has a negative impact on search performance with its only benefit being the ability to use heuristics that have no axiom support. As a compromise between performance and expressivity, we identify axioms of a simple form and devise a compilation for them. We compile away all axioms in several of the tested domains without a decline in search performance.

The International Planning Competitions (IPCs) serve as a testing suite for planning sys- tems. These domains are well-motivated as they are derived from, or possess characteristics analogous to real-life applications. In this thesis, we study the computational complexity of the plan existence and bounded plan existence decision problems of the following grid- based IPC domains: VisitAll, TERMES, Tidybot, Floortile, and Nurikabe. In all of these domains, there are one or more agents moving through a rectangular grid (potentially with obstacles) performing actions along the way. In many cases, we engineer instances that can be solved only if the movement of the agent or agents follows a Hamiltonian path or cycle in a grid graph. This gives rise to many NP-hardness reductions from Hamiltonian path/cycle problems on grid graphs. In the case of VisitAll and Floortile, we give necessary and suffi- cient conditions for deciding the plan existence problem in polynomial time. We also show that Tidybot has the game Push -1F as a special case, and its plan existence problem is thus PSPACE-complete. The hardness proofs in this thesis highlight hard instances of these domains. Moreover, by assigning a complexity class to each domain, researchers and practitioners can better assess the strengths and limitations of new and existing algorithms in these domains.

Planning tasks can be used to describe many real world problems of interest. Solving those tasks optimally is thus an avenue of great interest. One established and successful approach for optimal planning is the merge-and-shrink framework, which decomposes the task into a factored transition system. The factors initially represent the behaviour of one state variable and are repeatedly combined and abstracted. The solutions of these abstract states is then used as a heuristic to guide search in the original planning task. Existing merge-and-shrink transformations keep the factored transition system orthogonal, meaning that the variables of the planning task are represented in no more than one factor at any point. In this thesis we introduce the clone transformation, which duplicates a factor of the factored transition system, making it non-orthogonal. We test two classes of clone strategies, which we introduce and implement in the Fast Downward planning system and conclude that, while theoretically promising, our clone strategies are practically inefficient as their performance was worse than state-of-the-art methods for merge-and-shrink.

This thesis aims to present a novel approach for improving the performance of classical planning algorithms by integrating cost partitioning with merge-and-shrink techniques. Cost partitioning is a well-known technique for admissibly adding multiple heuristic values. Merge-and-shrink, on the other hand, is a technique to generate well-informed abstractions. The "merge” part of the technique is based on creating an abstract representation of the original problem by replacing two transition systems with their synchronised product. In contrast, the ”shrink” part refers to reducing the size of the factor. By combining these two approaches, we aim to leverage the strengths of both methods to achieve better scalability and efficiency in solving classical planning problems. Considering a range of benchmark domains and the Fast Downward planning system, the experimental results show that the proposed method achieves the goal of fusing merge and shrink with cost partitioning towards better outcomes in classical planning.

Planning is the process of finding a path in a planning task from the initial state to a goal state. Multiple algorithms have been implemented to solve such planning tasks, one of them being the Property-Directed Reachability algorithm. Property-Directed Reachability utilizes a series of propositional formulas called layers to represent a super-set of states with a goal distance of at most the layer index. The algorithm iteratively improves the layers such that they represent a minimum number of states. This happens by strengthening the layer formulas and therefore excluding states with a goal distance higher than the layer index. The goal of this thesis is to implement a pre-processing step to seed the layers with a formula that already excludes as many states as possible, to potentially improve the run-time performance. We use the pattern database heuristic and its associated pattern generators to make use of the planning task structure for the seeding algorithm. We found that seeding does not consistently improve the performance of the Property-Directed Reachability algorithm. Although we observed a significant reduction in planning time for some tasks, it significantly increased for others.

Certifying algorithms is a concept developed to increase trust by demanding affirmation of the computed result in form of a certificate. By inspecting the certificate, it is possible to determine correctness of the produced output. Modern planning systems have been certifying for long time in the case of solvable instances, where a generated plan acts as a certificate.

Only recently there have been the first steps towards certifying unsolvability judgments in the form of inductive certificates which represent certain sets of states. Inductive certificates are expressed with the help of propositional formulas in a specific formalism.

In this thesis, we investigate the use of propositional formulas in conjunctive normal form (CNF) as a formalism for inductive certificates. At first, we look into an approach that allows us to construct formulas representing inductive certificates in CNF. To show general applicability of this approach, we extend this to the family of delete relaxation heuristics. Furthermore, we present how a planning system is able to generate an inductive validation formula, a single formula that can be used to validate if the set found by the planner is indeed an inductive certificate. At last, we show with an experimental evaluation that the CNF formalism can be feasible in practice for the generation and validation of inductive validation formulas.

In generalized planning the aim is to solve whole classes of planning tasks instead of single tasks one at a time. Generalized representations provide information or knowledge about such classes to help solving them. This work compares the expressiveness of three generalized representations, generalized potential heuristics, policy sketches and action schema networks, in terms of compilability. We use a notion of equivalence that requires two generalized representations to decompose the tasks of a class into the same subtasks. We present compilations between pairs of equivalent generalized representations and proofs where a compilation is impossible.

A Digital Microfluidic Biochip (DMFB) is a digitally controllable lab-on-a-chip. Droplets of fluids are moved, merged and mixed on a grid. Routing these droplets efficiently has been tackled by various different approaches. We try to use temporal planning to do droplet routing, inspired by the use of it in quantum circuit compilation. We test a model for droplet routing in both classical and temporal planning and compare both versions. We show that our classical planning model is an efficient method to find droplet routes on DMFBs. Then we extend our model and include spawning, disposing, merging, splitting and mixing of droplets. The results of these extensions show that we are able to find plans for simple experiments. When scaling the problem size to real life experiments our model fails to find plans.

Cost partitioning is a technique used to calculate heuristics in classical optimal planning. It involves solving a linear program. This linear program can be decomposed into a master and pricing problems. In this thesis we combine Fourier-Motzkin elimination and the double description method in different ways to precompute the generating rays of the pricing problems. We further empirically evaluate these approaches and propose a new method that replaces the Fourier-Motzkin elimination. Our new method improves the performance of our approaches with respect to runtime and peak memory usage.

The increasing number of data nowadays has contributed to new scheduling approaches. Aviation is one of the domains concerned the most, as the aircraft engine implies millions of maintenance events operated by staff worldwide. In this thesis we present a constraint programming-based algorithm to solve the aircraft maintenance scheduling problem. We want to find the best time to do the maintenance by determining which employee will perform the work and when. Here we report how the scheduling process in aviation can be automatized.

To solve stochastic state-space tasks, the research field of artificial intelligence is mainly used. PROST2014 is state of the art when determining good actions in an MDP environment. In this thesis, we aimed to provide a heuristic by using neural networks to outperform the dominating planning system PROST2014. For this purpose, we introduced two variants of neural networks that allow to estimate the respective Q-value for a pair of state and action. Since we envisaged the learning method of supervised learning, in addition to the architecture as well as the components of the neural networks, the generation of training data was also one of the main tasks. To determine the most suitable network parameters, we performed a sequential parameter search, from which we expected a local optimum of the model settings. In the end, the PROST2014 planning system could not be surpassed in the total rating evaluation. Nevertheless, in individual domains, we could establish increased final scores on the side of the neural networks. The result shows the potential of this approach and points to eventual adaptations in future work pursuing this procedure furthermore.

In classical planning, there are tasks that are hard and tasks that are easy. We can measure the complexity of a task with the correlation complexity, the improvability width, and the novelty width. In this work, we compare these measures.

We investigate what causes a correlation complexity of at least 2. To do so we translate the state space into a vector space which allows us to make use of linear algebra and convex cones.

Additionally, we introduce the Basel measure, a new measure that is based on potential heuristics and therefore similar to the correlation complexity but also comparable to the novelty width. We show that the Basel measure is a lower bound for the correlation complexity and that the novelty width +1 is an upper bound for the Basel measure.

Furthermore, we compute the Basel measure for some tasks of the International Planning Competitions and show that the translation of a task can increase the Basel measure by removing seemingly irrelevant state variables.

Unsolvability is an important result in classical planning and has seen increased interest in recent years. This thesis explores unsolvability detection by automatically generating parity arguments, a well-known way of proving unsolvability. The argument requires an invariant measure, whose parity remains constant across all reachable states, while all goal states are of the opposite parity. We express parity arguments using potential functions in the field F 2 . We develop a set of constraints that describes potential functions with the necessary separating property, and show that the constraints can be represented efficiently for up to two-dimensional features. Enhanced with mutex information, an algorithm is formed that tests whether a parity function exists for a given planning task. The existence of such a function proves the task unsolvable. To determine its practical use, we empirically evaluate our approach on a benchmark of unsolvable problems and compare its performance to a state of the art unsolvability planner. We lastly analyze the arguments found by our algorithm to confirm their validity, and understand their expressive power.

We implemented the invariant synthesis algorithm proposed by Rintanen and experimentally compared it against Helmert’s mutex group synthesis algorithm as implemented in Fast Downward.

The context for the comparison is the translation of propositional STRIPS tasks to FDR tasks, which requires the identification of mutex groups.

Because of its dominating lead in translation speed, combined with few and marginal advantages in performance during search, Helmert’s algorithm is clearly better for most uses. Meanwhile Rintanen’s algorithm is capable of finding invariants other than mutexes, which Helmert’s algorithm per design cannot do.

The International Planning Competition (IPC) is a competition of state-of-the-art planning systems. The evaluation of these planning systems is done by measuring them with different problems. It focuses on the challenges of AI planning by analyzing classical, probabilistic and temporal planning and by presenting new problems for future research. Some of the probabilistic domains introduced in IPC 2018 are Academic Advising, Chromatic Dice, Cooperative Recon, Manufacturer, Push Your Luck, Red-finned Blue-eyes, etc.

This thesis aims to solve (near)-optimally two probabilistic IPC 2018 domains, Academic Advising and Chromatic Dice. We use different techniques to solve these two domains. In Academic Advising, we use a relevance analysis to remove irrelevant actions and state variables from the planning task. We then convert the problem from probabilistic to classical planning, which helped us solve it efficiently. In Chromatic Dice, we implement backtracking search to solve the smaller instances optimally. More complex instances are partitioned into several smaller planning tasks, and a near-optimal policy is derived as a combination of the optimal solutions to the small instances.

The motivation for finding (near)-optimal policies is related to the IPC score, which measures the quality of the planners. By providing the optimal upper bound of the domains, we contribute to the stabilization of the IPC score evaluation metric for these domains.

Most well-known and traditional online planners for probabilistic planning are in some way based on Monte-Carlo Tree Search. SOGBOFA, symbolic online gradient-based optimization for factored action MDPs, offers a new perspective on this: it constructs a function graph encoding the expected reward for a given input state using independence assumptions for states and actions. On this function, they use gradient ascent to perform a symbolic search optimizing the actions for the current state. This unique approach to probabilistic planning has shown very strong results and even more potential. In this thesis, we attempt to integrate the new ideas SOGBOFA presents into the traditionally successful Trial-based Heuristic Tree Search framework. Specifically, we design and evaluate two heuristics based on the aforementioned graph and its Q value estimations, but also the search using gradient ascent. We implement and evaluate these heuristics in the Prost planner, along with a version of the current standalone planner.

In this thesis, we consider cyclical dependencies between landmarks for cost-optimal planning. Landmarks denote properties that must hold at least once in all plans. However, if the orderings between them induce cyclical dependencies, one of the landmarks in each cycle must be achieved an additional time. We propose the generalized cycle-covering heuristic which considers this in addition to the cost for achieving all landmarks once.

Our research is motivated by recent applications of cycle-covering in the Freecell and logistics domain where it yields near-optimal results. We carry it over to domain-independent planning using a linear programming approach. The relaxed version of a minimum hitting set problem for the landmarks is enhanced by constraints concerned with cyclical dependencies between them. In theory, this approach surpasses a heuristic that only considers landmarks.

We apply the cycle-covering heuristic in practice where its theoretical dominance is confirmed; Many planning tasks contain cyclical dependencies and considering them affects the heuristic estimates favorably. However, the number of tasks solved using the improved heuristic is virtually unaffected. We still believe that considering this feature of landmarks offers great potential for future work.

Potential heuristics are a class of heuristics used in classical planning to guide a search algorithm towards a goal state. Most of the existing research on potential heuristics is focused on finding heuristics that are admissible, such that they can be used by an algorithm such as A* to arrive at an optimal solution. In this thesis, we focus on the computation of potential heuristics for satisficing planning, where plan optimality is not required and the objective is to find any solution. Specifically, our focus is on the computation of potential heuristics that are descending and dead-end avoiding (DDA), since these prop- erties guarantee favorable search behavior when used with greedy search algorithms such as hillclimbing. We formally prove that the computation of DDA heuristics is a PSPACE-complete problem and propose several approximation algorithms. Our evaluation shows that the resulting heuristics are competitive with established approaches such as Pattern Databases in terms of heuristic quality but suffer from several performance bottlenecks.

Most automated planners use heuristic search to solve the tasks. Usually, the planners get as input a lifted representation of the task in PDDL, a compact formalism describing the task using a fragment of first-order logic. The planners then transform this task description into a grounded representation where the task is described in propositional logic. This new grounded format can be exponentially larger than the lifted one, but many planners use this grounded representation because it is easier to implement and reason about.

However, sometimes this transformation between lifted and grounded representations is not tractable. When this is the case, there is not much that planners based on heuristic search can do. Since this transformation is a required preprocess, when this fails, the whole planner fails.

To solve the grounding problem, we introduce new methods to deal with tasks that cannot be grounded. Our work aims to find good ways to perform heuristic search while using a lifted representation of planning problems. We use the point-of-view of planning as a database progression problem and borrow solutions from the areas of relational algebra and database theory.

Our theoretical and empirical results are motivating: several instances that were never solved by any planner in the literature are now solved by our new lifted planner. For example, our planner can solve the challenging Organic Synthesis domain using a breadth-first search, while state-of-the-art planners cannot solve more than 60% of the instances. Furthermore, our results offer a new perspective and a deep theoretical study of lifted representations for planning tasks.

The generation of independently verifiable proofs for the unsolvability of planning tasks using different heuristics, including linear Merge-and-Shrink heuristics, is possible by usage of a proof system framework. Proof generation in the case of non-linear Merge-and-Shrink heuristic, however, is currently not supported. This is due to the lack of a suitable state set representation formalism that allows to compactly represent states mapped to a certain value in the belonging Merge-and-Shrink representation (MSR). In this thesis, we overcome this shortcoming using Sentential Decision Diagrams (SDDs) as set representations. We describe an algorithm that constructs the desired SDD from the MSR, and show that efficient proof verification is possible with SDDs as representation formalism. Aditionally, we use a proof of concept implementation to analyze the overhead occurred by the proof generation functionality and the runtime of the proof verification.

The operator-counting framework is a framework in classical planning for heuristics that are based on linear programming. The operator-counting framework covers several kinds of state-of-the-art linear programming heuristics, among them the post-hoc optimization heuristic. In this thesis we will use post-hoc optimization constraints and evaluate them under altered cost functions instead of the original cost function of the planning task. We show that such cost-altered post-hoc optimization constraints are also covered by the operator-counting framework and that it is possible to achieve improved heuristic estimates with them, compared with post-hoc optimization constraints under the original cost function. In our experiments we have not been able to achieve improved problem coverage, as we were not able to find a method for generating favorable cost functions that work well in all domains.

Heuristic forward search is the state-of-the-art approach to solve classical planning problems. On the other hand, bidirectional heuristic search has a lot of potential but was never able to deliver on those expectations in practice. Only recently the near-optimal bidirectional search algorithm (NBS) was introduces by Chen et al. and as the name suggests, NBS expands nearly the optimal number of states to solve any search problem. This is a novel achievement and makes the NBS algorithm a very promising and efficient algorithm in search. With this premise in mind, we raise the question of how applicable NBS is to planning. In this thesis, we inquire this very question by implementing NBS in the state- of-the-art planner Fast-Downward and analyse its performance on the benchmark of the latest international planning competition. We additionally implement fractional meet-in- the-middle and computeWVC to analyse NBS’ performance more thoroughly in regards to the structure of the problem task.

The conducted experiments show that NBS can successfully be applied to planning as it was able to consistently outperform A*. Especially good results were achieved on the domains: blocks, driverlog, floortile-opt11-strips, get-opt14-strips, logistics00, and termes- opt18-strips. Analysing these results, we deduce that the efficiency of forward and backward search depends heavily upon the underlying implicit structure of the transition system which is induced by the problem task. This suggests that bidirectional search is inherently more suited for certain problems. Furthermore, we find that this aptitude for a certain search direction correlates with the domain, thereby providing a powerful analytic tool to a priori derive the effectiveness of certain search approaches.

In conclusion, even without intricate improvements the NBS algorithm is able to compete with A*. It therefore has further potential for future research. Additionally, the underlying transition system of a problem instance is shown to be an important factor which influences the efficiency of certain search approaches. This knowledge could be valuable for devising portfolio planners.

Multiple Sequence Alignment (MSA) is the problem of aligning multiple biological sequences in the evoluationary most plausible way. It can be viewed as a shortest path problem through an n-dimensional lattice. Because of its large branching factor of 2^n − 1, it has found broad attention in the artificial intelligence community. Finding a globally optimal solution for more than a few sequences requires sophisticated heuristics and bounding techniques in order to solve the problem in acceptable time and within memory limitations. In this thesis, we show how existing heuristics fall into the category of combining certain pattern databases. We combine arbitrary pattern collections that can be used as heuristic estimates and apply cost partitioning techniques from classical planning for MSA. We implement two of those heuristics for MSA and compare their estimates to the existing heuristics.

Increasing Cost Tree Search is a promising approach to multi-agent pathfinding problems, but like all approaches it has to deal with a huge number of possible joint paths, growing exponentially with the number of agents. We explore the possibility of reducing this by introducing a value abstraction to the Multi-valued Decision Diagrams used to represent sets of joint paths. To that end we introduce a heat map to heuristically judge how collisionprone agent positions are and present how to use and possible refine abstract positions in order to still find valid paths.

Estimating cheapest plan costs with the help of network flows is an established technique. Plans and network flows are already very similar, however network flows can differ from plans in the presence of cycles. If a transition system contains cycles, flows might be composed of multiple disconnected parts. This discrepancy can make the cheapest plan estimation worse. One idea to get rid of the cycles works by introducing time steps. For every time step the states of a transition system are copied. Transitions will be changed, so that they connect states only with states of the next time step, which ensures that there are no cycles. It turned out, that by applying this idea to multiple transitions systems, network flows of the individual transition systems can be synchronized via the time steps to get a new kind of heuristic, that will also be discussed in this thesis.

Probabilistic planning is a research field that has become popular in the early 1990s. It aims at finding an optimal policy which maximizes the outcome of applying actions to states in an environment that feature unpredictable events. Such environments can consist of a large number of states and actions which make finding an optimal policy intractable using classical methods. Using a heuristic function for a guided search allows for tackling such problems. Designing a domain-independent heuristic function requires complex algorithms which may be expensive when it comes to time and memory consumption.

In this thesis, we are applying the supervised learning techniques for learning two domain-independent heuristic functions. We use three types of gradient descent methods: stochastic, batch and mini-batch gradient descent and their improved versions using momen- tum, learning decay rate and early stopping. Furthermore, we apply the concept of feature combination in order to better learn the heuristic functions. The learned functions are pro- vided to Prost, a domain-independent probabilistic planner, and benchmarked against the winning algorithms of the International Probabilistic Planning Competition held in 2014. The experiments show that learning an offline heuristic improves the overall score of the search for some of the domains used in aforementioned competition.

The merge-and-shrink heuristic is a state-of-the-art admissible heuristic that is often used for optimal planning. Recent studies showed that the merge strategy is an important factor for the performance of the merge-and-shrink algorithm. There are many different merge strategies and improvements for merge strategies described in the literature. One out of these merge strategies is MIASM by Fan et al. MIASM tries to merge transition systems that produce unnecessary states in their product which can be pruned. Another merge strategy is the symmetry-based merge-and-shrink framework by Sievers et al. This strategy tries to merge transition systems that cause factored symmetries in their product. This strategy can be combined with other merge strategies and it often improves the performance for many merge strategy. However, the current combination of MIASM with factored symmetries performs worse than MIASM. We implement a different combination of MIASM that uses factored symmetries during the subset search of MIASM. Our experimental evaluation shows that our new combination of MIASM with factored symmetries solves more tasks than the existing MIASM and the previously implemented combination of MIASM with factored symmetries. We also evaluate different combinations of existing merge strategies and find combinations that perform better than their basic version that were not evaluated before.

Tree Cache is a pathfinding algorithm that selects one vertex as a root and constructs a tree with cheapest paths to all other vertices. A path is found by traversing up the tree from both the start and goal vertices to the root and concatenating the two parts. This is fast, but as all paths constructed this way pass through the root vertex they can be highly suboptimal.

To improve this algorithm, we consider two simple approaches. The first is to construct multiple trees, and save the distance to each root in each vertex. To find a path, the algorithm first selects the root with the lowest total distance. The second approach is to remove redundant vertices, i.e. vertices that are between the root and the lowest common ancestor (LCA) of the start and goal vertices. The performance and space requirements of the resulting algorithm are then compared to the conceptually similar hub labels and differential heuristics.

Greedy Best-First Search (GBFS) is a prominent search algorithm to find solutions for planning tasks. GBFS chooses nodes for further expansion based on a distance-to-goal estimator, the heuristic. This makes GBFS highly dependent on the quality of the heuristic. Heuristics often face the problem of producing Uninformed Heuristic Regions (UHRs). GBFS additionally suffers the possibility of simultaneously expanding nodes in multiple UHRs. In this thesis we change the heuristic approach in UHRs. The heuristic was unable to guide the search and so we try to expand novel states to escape the UHRs. The novelty measures how “new” a state is in the search. The result is a combination of heuristic and novelty guided search, which is indeed able to escape UHRs quicker and solve more problems in reasonable time.

In classical AI planning, the state explosion problem is a reoccurring subject: although the problem descriptions are compact, often a huge number of states needs to be considered. One way to tackle this problem is to use static pruning methods which reduce the number of variables and operators in the problem description before planning.

In this work, we discuss the properties and limitations of three existing static pruning techniques with a focus on satisficing planning. We analyse these pruning techniques and their combinations, and identify synergy effects between them and the domains and problem structures in which they occur. We implement the three methods into an existing propositional planner, and evaluate the performance of different configurations and combinations in a set of experiments on IPC benchmarks. We observe that static pruning techniques can increase the number of solved problems, and that the synergy effects of the combinations also occur on IPC benchmarks, although they do not lead to a major performance increase.

The goal of classical domain-independent planning is to find a sequence of actions which lead from a given initial state to a goal state that satisfies some goal criteria. Most planning systems use heuristic search algorithms to find such a sequence of actions. A critical part of heuristic search is the heuristic function. In order to find a sequence of actions from an initial state to a goal state efficiently this heuristic function has to guide the search towards the goal. It is difficult to create such an efficient heuristic function. Arfaee et al. show that it is possible to improve a given heuristic function by applying machine learning techniques on a single domain in the context of heuristic search. To achieve this improvement of the heuristic function, they propose a bootstrap learning approach which subsequently improves the heuristic function.

In this thesis we will introduce a technique to learn heuristic functions that can be used in classical domain-independent planning based on the bootstrap-learning approach introduced by Arfaee et al. In order to evaluate the performance of the learned heuristic functions, we have implemented a learning algorithm for the Fast Downward planning system. The experiments have shown that a learned heuristic function generally decreases the number of explored states compared to blind-search . The total time to solve a single problem increases because the heuristic function has to be learned before it can be applied.

Essential for the estimation of the performance of an algorithm in satisficing planning is its ability to solve benchmark problems. Those results can not be compared directly as they originate from different implementations and different machines. We implemented some of the most promising algorithms for greedy best-first search, published in the last years, and evaluated them on the same set of benchmarks. All algorithms are either based on randomised search, localised search or a combination of both. Our evaluation proves the potential of those algorithms.

Heuristic search with admissible heuristics is the leading approach to cost-optimal, domain-independent planning. Pattern database heuristics - a type of abstraction heuristics - are state-of-the-art admissible heuristics. Two recent pattern database heuristics are the iPDB heuristic by Haslum et al. and the PhO heuristic by Pommerening et al.

The iPDB procedure performs a hill climbing search in the space of pattern collections and evaluates selected patterns using the canonical heuristic. We apply different techniques to the iPDB procedure, improving its hill climbing algorithm as well as the quality of the resulting heuristic. The second recent heuristic - the PhO heuristic - obtains strong heuristic values through linear programming. We present different techniques to influence and improve on the PhO heuristic.

We evaluate the modified iPDB and PhO heuristics on the IPC benchmark suite and show that these abstraction heuristics can compete with other state-of-the-art heuristics in cost-optimal, domain-independent planning.

Greedy best-first search (GBFS) is a prominent search algorithm for satisficing planning - finding good enough solutions to a planning task in reasonable time. GBFS selects the next node to consider based on the most promising node estimated by a heuristic function. However, this behaviour makes GBFS heavily depend on the quality of the heuristic estimator. Inaccurate heuristics can lead GBFS into regions far away from a goal. Additionally, if the heuristic ranks several nodes the same, GBFS has no information on which node it shall follow. Diverse best-first search (DBFS) is a new algorithm by Imai and Kishimoto [2011] which has a local search component to emphasis exploitation. To enable exploration, DBFS deploys probabilities to select the next node.

In two problem domains, we analyse GBFS' search behaviour and present theoretical results. We evaluate these results empirically and compare DBFS and GBFS on constructed as well as on provided problem instances.

State-of-the-art planning systems use a variety of control knowledge in order to enhance the performance of heuristic search. Unfortunately most forms of control knowledge use a specific formalism which makes them hard to combine. There have been several approaches which describe control knowledge in Linear Temporal Logic (LTL). We build upon this work and propose a general framework for encoding control knowledge in LTL formulas. The framework includes a criterion that any LTL formula used in it must fulfill in order to preserve optimal plans when used for pruning the search space; this way the validity of new LTL formulas describing control knowledge can be checked. The framework is implemented on top of the Fast Downward planning system and is tested with a pruning technique called Unnecessary Action Application, which detects if a previously applied action achieved no useful progress.

Landmarks are known to be useable for powerful heuristics for informed search. In this thesis, we explain and evaluate a novel algorithm to find ordered landmarks of delete free tasks by intersecting solutions in the relaxation. The proposed algorithm efficiently finds landmarks and natural orders of delete free tasks, such as delete relaxations or Pi-m compilations.

Planning as heuristic search is the prevalent technique to solve planning problems of any kind of domains. Heuristics estimate distances to goal states in order to guide a search through large state spaces. However, this guidance is sometimes moderate, since still a lot of states lie on plateaus of equally prioritized states in the search space topology. Additional techniques that ignore or prefer some actions for solving a problem are successful to support the search in such situations. Nevertheless, some action pruning techniques lead to incomplete searches.

We propose an under-approximation refinement framework for adding actions to under-approximations of planning tasks during a search in order to find a plan. For this framework, we develop a refinement strategy. Starting a search on an initial under-approximation of a planning task, the strategy adds actions determined at states close to a goal, whenever the search does not progress towards a goal, until a plan is found. Key elements of this strategy consider helpful actions and relaxed plans for refinements. We have implemented the under-approximation refinement framework into the greedy best first search algorithm. Our results show considerable speedups for many classical planning problems. Moreover, we are able to plan with fewer actions than standard greedy best first search.

The main approach for classical planning is heuristic search. Many cost heuristics are based on the delete relaxation. The optimal heuristic of a delete free planning problem is called h + . This thesis explores two new ways to compute h + . Both approaches use factored planning, which decomposes the original planning problem to work on each subproblem separately. The algorithm reuses the subsolutions and combines them to a global solution.

The two algorithms are used to compute a cost heuristic for an A* search. As both approaches compute the optimal heuristic for delete free planning tasks, the algorithms can also be used to find a solution for relaxed planning tasks.

Multi-Agent-Path-Finding (MAPF) is a common problem in robotics and memory management. Pebbles in Motion is an implementation of a problem solver for MAPF in polynomial time, based on a work by Daniel Kornhauser from 1984. Recently a lot of research papers have been published on MAPF in the research community of Artificial Intelligence, but the work by Kornhauser seems hardly to be taken into account. We assumed that this might be related to the fact that said paper was more mathematically and hardly describing algorithms intuitively. This work aims at filling this gap, by providing an easy understandable approach of implementation steps for programmers and a new detailed description for researchers in Computer Science.

Bachelor's theses

Fast Downward is a classical planner using heuristical search. The planner uses many advanced planning techniques that are not easy to teach, since they usually rely on complex data structures. To introduce planning techniques to the user an interactive application is created. This application uses an illustrative example to showcase planning techniques: Blocksworld

Blocksworld is an easy understandable planning problem which allows a simple representation of a state space. It is implemented in the Unreal Engine and provides an interface to the Fast Downward planner. Users can explore a state space themselves or have Fast Downward generate plans for them. The concept of heuristics as well as the state space are explained and made accessible to the user. The user experiences how the planner explores a state space and which techniques the planner uses.

This thesis is about implementing Jussi Rintanen’s algorithm for schematic invariants. The algo- rithm is implemented in the planning tool Fast Downward and refers to Rintanen’s paper Schematic Invariants by Reduction to Ground Invariants. The thesis describes all necessary definitions to under- stand the algorithm and draws a comparison between the original task and a reduced task in terms of runtime and number of grounded actions.

Planning is a field of Artificial Intelligence. Planners are used to find a sequence of actions, to get from the initial state to a goal state. Many planning algorithms use heuristics, which allow the planner to focus on more promising paths. Pattern database heuristics allow us to construct such a heuristic, by solving a simplified version of the problem, and saving the associated costs in a pattern database. These pattern databases can be computed and stored by using symbolic data structures.

In this paper we will look at how pattern databases using symbolic data structures using binary decision diagrams and algebraic decision diagrams can be implemented. We will extend fast down- ward (Helmert [2006]) with it, and compare the performance of this implementation with the already implemented explicit pattern database.

In the field of automated planning and scheduling, a planning task is essentially a state space which can be defined rigorously using one of several different formalisms (e.g. STRIPS, SAS+, PDDL etc.). A planning algorithm tries to determine a sequence of actions that lead to a goal state for a given planning task. In recent years, attempts have been made to group certain planners together into so called planner portfolios, to try and leverage their effectiveness on different specific problem classes. In our project, we create an online planner which in contrast to its offline counterparts, makes use of task specific information when allocating a planner to a task. One idea that has recently gained interest, is to apply machine learning methods to planner portfolios.

In previous work such as Delfi (Katz et al., 2018; Sievers et al., 2019a) supervised learning techniques were used, which made it necessary to train multiple networks to be able to attempt multiple, potentially different, planners for a given task. The reason for this being that, if we used the same network, the output would always be the same, as the input to the network would remain unchanged. In this project we make use of techniques from rein- forcement learning such as DQNs (Mnih et al., 2013). Using RL approaches such as DQNs, allows us to extend the input to the network to include information on things, such as which planners were previously attempted and for how long. As a result multiple attempts can be made after only having trained a single network.

Unfortunately the results show that current reinforcement learning agents are, amongst other reasons, too sample inefficient to be able to deliver viable results given the size of the currently available data sets.

Planning tasks are important and difficult problems in computer science. A widely used approach is the use of delete relaxation heuristics to which the additive and FF heuristic belong. Those two heuristics use a graph in their calculation, which only has to be constructed once for a planning task but then can be used repeatedly. To solve such a problem efficiently it is important that the calculation of the heuristics are fast. In this thesis the idea to achieve a faster calculation is to combine redundant parts of the graph when building it to reduce the number of edges and therefore speed up the calculation. Here the reduction of the redundancies is done for each action within a planning task individually, but further ideas to simplify over all actions are also discussed.

Monte Carlo search methods are widely known, mostly for their success in game domains, although they are also applied to many non-game domains. In previous work done by Schulte and Keller, it was established that best-first searches could adapt to the action selection functionality which make Monte Carlo methods so formidable. In practice however, the trial-based best first search, without exploration, was shown to be slightly slower than its explicit open list counterpart. In this thesis we examine the non-trial and trial-based searches and how they can address the exploitation exploration dilemma. Lastly, we will see how trial-based BFS can rectify a slower search by allowing occasional random action selection, by comparing it to regular open list searches in a line of experiments.

Sudoku has become one of the world’s most popular logic puzzles, arousing interest in the general public and the science community. Although the rules of Sudoku may seem simple, they allow for nearly countless puzzle instances, some of which are very hard to solve. SAT-solvers have proven to be a suitable option to solve Sudokus automatically. However, they demand the puzzles to be encoded as logical formulae in Conjunctive Normal Form. In earlier work, such encodings have been successfully demonstrated for original Sudoku Puzzles. In this thesis, we present encodings for rather unconventional Sudoku Variants, developed by the puzzle community to create even more challenging solving experiences. Furthermore, we demonstrate how Pseudo-Boolean Constraints can be utilized to encode Sudoku Variants that follow rules involving sums. To implement an encoding of Pseudo-Boolean Constraints, we use Binary Decision Diagrams and Adder Networks and study how they compare to each other.

In optimal classical planning, informed search algorithms like A* need admissible heuristics to find optimal solutions. Counterexample-guided abstraction refinement (CEGAR) is a method used to generate abstractions that yield suitable abstraction heuristics iteratively. In this thesis, we propose a class of CEGAR algorithms for the generation of domain abstractions, which are a class of abstractions that rank in between projections and Cartesian abstractions regarding the grade of refinement they allow. As no known algorithm constructs domain abstractions, we show that our algorithm is competitive with CEGAR algorithms that generate one projection or Cartesian abstraction.

This thesis will look at Single-Player Chess as a planning domain using two approaches: one where we look at how we can encode the Single-Player Chess problem as a domain-independent (general-purpose AI) approach and one where we encode the problem as a domain-specific solver. Lastly, we will compare the two approaches by doing some experiments and comparing the results of the two approaches. Both the domain-independent implementation and the domain-specific implementation differ from traditional chess engines because the task of the agent is not to find the best move for a given position and colour, but the agent’s task is to check if a given chess problem has a solution or not. If the agent can find a solution, the given chess puzzle is valid. The results of both approaches were measured in experiments, and we found out that the domain-independent implementation is too slow and that the domain-specific implementation, on the other hand, can solve the given puzzles reliably, but it has a memory bottleneck rooted in the search method that was used.

Carcassonne is a tile-based board game with a large state space and a high branching factor and therefore poses a challenge to artificial intelligence. In the past, Monte Carlo Tree Search (MCTS), a search algorithm for sequential decision-making processes, has been shown to find good solutions in large state spaces. MCTS works by iteratively building a game tree according to a tree policy. The profitability of paths within that tree is evaluated using a default policy, which influences in what directions the game tree is expanded. The functionality of these two policies, as well as other factors, can be implemented in many different ways. In consequence, many different variants of MCTS exist. In this thesis, we applied MCTS to the domain of two-player Carcassonne and evaluated different variants in regard to their performance and runtime. We found significant differences in performance for various variable aspects of MCTS and could thereby evaluate a configuration which performs best on the domain of Carcassonne. This variant consistently outperformed an average human player with a feasible runtime.

In general, it is important to verify software as it is prone to error. This also holds for solving tasks in classical planning. So far, plans in general as well as the fact that there is no plan for a given planning task can be proven and independently verified. However, no such proof for the optimality of a solution of a task exists. Our aim is to introduce two methods with which optimality can be proven and independently verified. We first reduce unit cost tasks to unsolvable tasks, which enables us to make use of the already existing certificates for unsolvability. In a second approach, we propose a proof system for optimality, which enables us to infer that the determined cost of a task is optimal. This permits the direct generation of optimality certificates.

Pattern databases are one of the most powerful heuristics in classical planning. They evaluate the perfect cost for a simplified sub-problem. The post-hoc optimization heuristic is a technique on how to optimally combine a set of pattern databases. In this thesis, we will adapt the post-hoc optimization heuristic for the sliding tile puzzle. The sliding tile puzzle serves as a benchmark to compare the post-hoc optimization heuristic to already established methods, which also deal with the combining of pattern databases. We will then show how the post-hoc optimization heuristic is an improvement over the already established methods.

In this thesis, we generate landmarks for a logistics-specific task. Landmarks are actions that need to occur at least once in every plan. A landmark graph denotes a structure with landmarks and their edges called orderings. If there are cycles in a landmark graph, one of those landmarks needs to be achieved at least twice for every cycle. The generation of the logistics-specific landmarks and their orderings calculate the cyclic landmark heuristic. The task is to pick up on related work, the evaluation of the cyclic landmark heuristic. We compare the generation of landmark graphs from a domain-independent landmark generator to a domain-specific landmark generator, the latter being the focus. We aim to bridge the gap between domain-specific and domain-independent landmark generators. In this thesis, we compare one domain-specific approach for the logistics domain with results from a domain- independent landmark generator. We devise a unit to pre-process data for other domain- specific tasks as well. We will show that specificity is better suited than independence.

Lineare Programmierung ist eine mathematische Modellierungstechnik, bei der eine lineare Funktion, unter der Berücksichtigung verschiedenen Beschränkungen, maximiert oder minimiert werden soll. Diese Technik ist besonders nützlich, falls Entscheidungen für Optimierungsprobleme getroffen werden sollen. Ziel dieser Arbeit war es ein Tool für das Spiel Factory Town zu entwickeln, mithilfe man Optimierungsanfragen bearbeiten kann. Dabei ist es möglich wahlweise zwischen diversen Fragestellungen zu wählen und anhand von LP-\ IP-Solvern diese zu beantworten. Zudem wurden die mathematischen Formulierungen, sowie die Unterschiede beider Methoden angegangen. Schlussendlich unterstrichen die generierten Resultate, dass LP Lösungen mindestens genauso gut oder sogar besser seien als die Lösungen eines IP.

Symbolic search is an important approach to classical planning. Symbolic search uses search algorithms that process sets of states at a time. For this we need states to be represented by a compact data structure called knowledge compilations. Merge-and-shrink representations come a different field of planning, where they have been used to derive heuristic functions for state-space search. More generally they represent functions that map variable assignments to a set of values, as such we can regard them as a data structure we will call Factored Mappings. In this thesis, we will investigate Factored Mappings (FMs) as a knowledge compilation language with the hope of using them for symbolic search. We will analyse the necessary transformations and queries for FMs, by defining the needed operations and a canonical representation of FMs, and showing that they run in polynomial time. We will then show that it is possible to use Factored Mappings as a knowledge compilation for symbolic search by defining a symbolic search algorithm for a finite-domain plannings task that works with FMs.

Version control systems use a graph data structure to track revisions of files. Those graphs are mutated with various commands by the respective version control system. The goal of this thesis is to formally define a model of a subset of Git commands which mutate the revision graph, and to model those mutations as a planning task in the Planning Domain Definition Language. Multiple ways to model those graphs will be explored and those models will be compared by testing them using a set of planners.

Pattern Databases are admissible abstraction heuristics for classical planning. In this thesis we are introducing the Boosting processes, which consists of enlarging the pattern of a Pattern Database P, calculating a more informed Pattern Database P' and then min-compress P' to the size of P resulting in a compressed and still admissible Pattern Database P''. We design and implement two boosting algorithms, Hillclimbing and Randomwalk.

We combine pattern database heuristics using five different cost partitioning methods. The experiments compare computing cost partitionings over regular and boosted pattern databases. The experiments, performed on IPC (optimal track) tasks, show promising results which increased the coverage (number of solved tasks) by 9 for canonical cost partitioning using our Randomwalk boosting variant.

One dimensional potential heuristics assign a numerical value, the potential, to each fact of a classical planning problem. The heuristic value of a state is the sum over the poten- tials belonging to the facts contained in the state. Fišer et al. (2020) recently proposed to strengthen potential heuristics utilizing mutexes and disambiguations. In this thesis, we embed the same enhancements in the planning system Fast Downward. The experi- mental evaluation shows that the strengthened potential heuristics are a refinement, but too computationally expensive to solve more problems than the non-strengthened potential heuristics.

The potentials are obtained with a Linear Program. Fišer et al. (2020) introduced an additional constraint on the initial state and we propose additional constraints on random states. The additional constraints improve the amount of solved problems by up to 5%.

This thesis discusses the PINCH heuristic, a specific implementation of the additive heuristic. PINCH intends to combine the strengths of existing implementations of the additive heuristic. The goal of this thesis is to really dig into the PINCH heuristic. I want to provide the most accessible resource for understanding PINCH and I want to analyze the performance of PINCH by comparing it to the algorithm on which it is based, Generalized Dijkstra.

Suboptimal search algorithms can offer attractive benefits compared to optimal search, namely increased coverage of larger search problems and quicker search times. Improving on such algorithms, such as reducing costs further towards optimal solutions and reducing the number of node expansions, is therefore a compelling area for further research. This paper explores the utility and scalability of recently developed priority functions, XDP, XUP, and PWXDP, and the Improved Optimistic Search algorithm, compared to Weighted A*, in the Fast Downward planner. Analyses focus on the cost, total time, coverage, and node expansion parameters, with experimental evidence suggesting preferable performance if strict optimality is not desired. The implementation of priorityb functions in eager best-first search showed marked improvements compared to A* search on coverage, total time, and number of expansions, without significant cost penalties. Following previous suboptimal search research, experimental evidence even seems to indicate that these cost penalties do not reach the designated bound, even in larger search spaces.

In the Automated Planning field, algorithms and systems are developed for exploring state spaces and ultimately finding an action sequence leading from a task’s initial state to its goal. Such planning systems may sometimes show unexpected behavior, caused by a planning task or a bug in the planner itself. Generally speaking, finding the source of a bug tends to be easier when the cause can be isolated or simplified. In this thesis, we tackle this problem by making PDDL and SAS+ tasks smaller while ensuring they still invoke a certain characteristic when executed with a planner. We implement a system that successively removes elements, such as objects, from a task and checks whether the transformed task still fails on the planner. Elements are removed in a syntactically consistent way, however, no semantic integrity is enforced. Our system’s design is centered around the Fast Downward Planning System, as we re-use some of its translator modules and all test runs are performed with Fast Downward. At the core of our system, first-choice hill-climbing is used for optimization. Our “minimizer” takes (1) a failing planner execution command, (2) a description of the failing characteristic and (3) the type of element to be deleted as arguments. We evaluate our system’s functionality on the basis of three use-cases. In our most successful test runs, (1) a SAS+ task with initially 1536 operators and 184 variables is reduced to 2 operators and 2 variables and (2)a PDDL task with initially 46 actions, 62 objects and 29 predicate symbols is reduced to 2 actions, 6 objects and 4 predicates.

Fast Downward is a classical planning system based on heuristic search. Its successor generator is an efficient and intelligent tool to process state spaces and generate their successor states. In this thesis we implement different successor generators in the Fast Downward planning system and compare them against each other. Apart from the given fast downward successor generator we implement four other successor generators: a naive successor generator, one based on the marking of delete relaxed heuristics, one based on the PSVN planning system and one based on watched literals as used in modern SAT solvers. These successor generators are tested in a variety of different planning benchmarks to see how well they compete against each other. We verified that there is a trade-off between precomputation and faster successor generation and showed that all of the implemented successor generators have a use case and it is advisable to switch to a successor generator that fits the style of the planning task.

Verifying whether a planning algorithm came to the correct result for a given planning task is easy if a plan is emitted which solves the problem. But if a task is unsolvable most planners just state this fact without any explanation or even proof. In this thesis we present extended versions of the symbolic search algorithms SymPA and symbolic bidirectional uniform-cost search which, if a given planning task is unsolvable, provide certificates which prove unsolvability. We also discuss a concrete implementation of this version of SymPA.

Classical planning is an attractive approach to solving problems because of its generality and its relative ease of use. Domain-specific algorithms are appealing because of their performance, but require a lot of resources to be implemented. In this thesis we evaluate concepts languages as a possible input language for expert domain knowledge into a planning system. We also explore mixed integer programming as a way to use this knowledge to improve search efficiency and to help the user find and refine useful domain knowledge.

Classical Planning is a branch of artificial intelligence that studies single agent, static, deterministic, fully observable, discrete search problems. A common challenge in this field is the explosion of states to be considered when searching for the goal. One technique that has been developed to mitigate this is Strong Stubborn Set based pruning, where on each state expansion, the considered successors are restricted to Strong Stubborn Sets, which exploit the properties of independent operators to cut down the tree or graph search. We adopt the definitions of the theory of Strong Stubborn Sets from the SAS+ setting to transition systems and validate a central theorem about the correctness of Strong Stubborn Set based pruning for transition systems in the interactive theorem prover Isabelle/HOL.

Ein wichtiges Feld in der Wissenschaft der künstliche Intelligenz sind Planungsprobleme. Man hat das Ziel, eine künstliche intelligente Maschine zu bauen, die mit so vielen ver- schiedenen Probleme umgehen und zuverlässig lösen kann, indem sie ein optimaler Plan herstellt.

Der Trial-based Heuristic Tree Search(THTS) ist ein mächtiges Werkzeug um Multi-Armed- Bandit-ähnliche Probleme, Marcow Decsision Processe mit verändernden Rewards, zu lösen. Beim momentanen THTS können explorierte gefundene gute Rewards auf Grund von der grossen Anzahl der Rewards nicht beachtet werden. Ebenso können beim explorieren schlech- te Rewards, gute Knoten im Suchbaum, verschlechtern. Diese Arbeit führt eine Methodik ein, die von der stückweise stationären MABs Problematik stammt, um den THTS weiter zu optimieren.

Abstractions are a simple yet powerful method of creating a heuristic to solve classical planning problems optimally. In this thesis we make use of Cartesian abstractions generated with Counterexample-Guided Abstraction Refinement (CEGAR). This method refines abstractions incrementally by finding flaws and then resolving them until the abstraction is sufficiently evolved. The goal of this thesis is to implement and evaluate algorithms which select solutions of such flaws, in a way which results in the best abstraction (that is, the abstraction which causes the problem to then be solved most efficiently by the planner). We measure the performance of a refinement strategy by running the Fast Downward planner on a problem and measuring how long it takes to generate the abstraction, as well as how many expansions the planner requires to find a goal using the abstraction as a heuristic. We use a suite of various benchmark problems for evaluation, and we perform this experiment for a single abstraction and on abstractions for multiple subtasks. Finally, we attempt to predict which refinement strategy should be used based on parameters of the task, potentially allowing the planner to automatically select the best strategy at runtime.

Heuristic search is a powerful paradigm in classical planning. The information generated by heuristic functions to guide the search towards a goal is a key component of many modern search algorithms. The paper “Using Backwards Generated Goals for Heuristic Planning” by Alcázar et al. proposes a way to make additional use of this information. They take the last actions of a relaxed plan as a basis to generate intermediate goals with a known path to the original goal. A plan is found when the forward search reaches an intermediate goal.

The premise of this thesis is to modify their approach by focusing on a single sequence of intermediate goals. The aim is to improve efficiency while preserving the benefits of backwards goal expansion. We propose different variations of our approach by introducing multiple ways to make decisions concerning the construction of intermediate goals. We evaluate these variations by comparing their performance and illustrate the challenges posed by this approach.

Counterexample-guided abstraction refinement (CEGAR) is a way to incrementally compute abstractions of transition systems. It starts with a coarse abstraction and then iteratively finds an abstract plan, checks where the plan fails in the concrete transition system and refines the abstraction such that the same failure cannot happen in subsequent iterations. As the abstraction grows in size, finding a solution for the abstract system becomes more and more costly. Because the abstraction grows incrementally, however, it is possible to maintain heuristic information about the abstract state space, allowing the use of informed search algorithms like A*. As the quality of the heuristic is crucial to the performance of informed search, the method for maintaining the heuristic has a significant impact on the performance of the abstraction refinement as a whole. In this thesis, we investigate different methods for maintaining the value of the perfect heuristic h* at all times and evaluate their performance.

Pattern Databases are a powerful class of abstraction heuristics which provide admissible path cost estimates by computing exact solution costs for all states of a smaller task. Said task is obtained by abstracting away variables of the original problem. Abstractions with few variables offer weak estimates, while introduction of additional variables is guaranteed to at least double the amount of memory needed for the pattern database. In this thesis, we present a class of algorithms based on counterexample-guided abstraction refinement (CEGAR), which exploit additivity relations of patterns to produce pattern collections from which we can derive heuristics that are both informative and computationally tractable. We show that our algorithms are competitive with already existing pattern generators by comparing their performance on a variety of planning tasks.

We consider the problem of Rubik’s Cube to evaluate modern abstraction heuristics. In order to find feasible abstractions of the enormous state space spanned by Rubik’s Cube, we apply projection in the form of pattern databases, Cartesian abstraction by doing counterexample guided abstraction refinement as well as merge-and-shrink strategies. While previous publications on Cartesian abstractions have not covered applicability for planning tasks with conditional effects, we introduce factorized effect tasks and show that Cartesian abstraction can be applied to them. In order to evaluate the performance of the chosen heuristics, we run experiments on different problem instances of Rubik’s Cube. We compare them by the initial h-value found for all problems and analyze the number of expanded states up to the last f-layer. These criteria provide insights about the informativeness of the considered heuristics. Cartesian Abstraction yields perfect heuristic values for problem instances close to the goal, however it is outperformed by pattern databases for more complex instances. Even though merge-and-shrink is the most general abstraction among the considered, it does not show better performance than the others.

Probabilistic planning expands on classical planning by tying probabilities to the effects of actions. Due to the exponential size of the states, probabilistic planners have to come up with a strong policy in a very limited time. One approach to optimising the policy that can be found in the available time is called metareasoning, a technique aiming to allocate more deliberation time to steps where more time to plan results in an improvement of the policy and less deliberation time to steps where an improvement of the policy with more time to plan is unlikely.

This thesis aims to adapt a recent proposal of a formal metareasoning procedure from Lin. et al. for the search algorithm BRTDP to work with the UCT algorithm in the Prost planner and compare its viability to the current standard and a number of less informed time management methods in order to find a potential improvement to the current uniform deliberation time distribution.

A planner tries to produce a policy that leads to a desired goal given the available range of actions and an initial state. A traditional approach for an algorithm is to use abstraction. In this thesis we implement the algorithm described in the ASAP-UCT paper: Abstraction of State-Action Pairs in UCT by Ankit Anand, Aditya Grover, Mausam and Parag Singla.

The algorithm combines state and state-action abstraction with a UCT-algorithm. We come to the conclusion that the algorithm needs to be improved because the abstraction of action-state often cannot detect a similarity that a reasonable action abstraction could find.

The notion of adding a form of exploration to guide a search has been proven to be an effective method of combating heuristical plateaus and improving the performance of greedy best-first search. The goal of this thesis is to take the same approach and introduce exploration in a bounded suboptimal search problem. Explicit estimation search (EES), established by Thayer and Ruml, consults potentially inadmissible information to determine the search order. Admissible heuristics are then used to guarantee the cost bound. In this work we replace the distance-to-go estimator used in EES with an approach based on the concept of novelty.

Classical domain-independent planning is about finding a sequence of actions which lead from an initial state to a goal state. A popular approach for solving planning problems efficiently is to utilize heuristic functions. A possible heuristic function is the perfect heuristic of a delete relaxed planning problem denoted as h+. Delete relaxation simplifies the planning problem thus making it easier to find a perfect heuristic. However computing h+ is still NP-hard problem.

In this thesis we discuss a promising looking approach to compute h+ in practice. Inspired by the paper from Gnad, Hoffmann and Domshlak about star-shaped planning problems, we implemented the Flow-Cut algorithm. The basic idea behind flow-cut to divide a problem that is unsolvable in practice, into smaller sub problems that can be solved. We further tested the flow-cut algorithm on the domains provided by the International Planning Competition benchmarks, resulting in the following conclusion: Using a divide and conquer approach can successfully be used to solve classical planning problems, however it is not trivial to design such an algorithm to be more efficient than state-of-the-art search algorithm.

This thesis deals with the algorithm presented in the paper "Landmark-based Meta Best-First Search Algorithm: First Parallelization Attempt and Evaluation" by Simon Vernhes, Guillaume Infantes and Vincent Vidal. Their idea was to reconsider the approach to landmarks as a tool in automated planning, but in a markedly different way than previous work had done. Their result is a meta-search algorithm which explores landmark orderings to find a series of subproblems that reliably lead to an effective solution. Any complete planner may be used to solve the subproblems. While the referenced paper also deals with an attempt to effectively parallelize the Landmark-based Meta Best-First Search Algorithm, this thesis is concerned mainly with the sequential implementation and evaluation of the algorithm in the Fast Downward planning system.

Heuristics play an important role in classical planning. Using heuristics during state space search often reduces the time required to find a solution, but constructing heuristics and using them to calculate heuristic values takes time, reducing this benefit. Constructing heuristics and calculating heuristic values as quickly as possible is very important to the effectiveness of a heuristic. In this thesis we introduce methods to bound the construction of merge-and-shrink to reduce its construction time and increase its accuracy for small problems and to bound the heuris- tic calculation of landmark cut to reduce heuristic value calculation time. To evaluate the performance of these depth-bound heuristics we have implemented them in the Fast Down- ward planning system together with three iterative-deepening heuristic search algorithms: iterative-deepening A* search, a new breadth-first iterative-deepening version of A* search and iterative-deepening breadth-first heuristic search.

Greedy best-first search has proven to be a very efficient approach to satisficing planning but can potentially lose some of its effectiveness due to the used heuristic function misleading it to a local minimum or plateau. This is where exploration with additional open lists comes in, to assist greedy best-first search with solving satisficing planning tasks more effectively. Building on the idea of exploration by clustering similar states together as described by Xie et al. [2014], where states are clustered according to heuristic values, we propose in this paper to instead cluster states based on the Hamming distance of the binary representation of states [Hamming, 1950]. The resulting open list maintains k buckets and inserts each given state into the bucket with the smallest average hamming distance between the already clustered states and the new state. Additionally, our open list is capable of reclustering all states periodically with the use of the k-means algorithm. We were able to achieve promising results concerning the amount of expansions necessary to reach a goal state, despite not achieving a higher coverage than fully random exploration due to slow performance. This was caused by the amount of calculations required to identify the most fitting cluster when inserting a new state.

Monte Carlo Tree Search Algorithms are an efficient method of solving probabilistic planning tasks that are modeled by Markov Decision Problems. MCTS uses two policies, a tree policy for iterating through the known part of the decission tree and a default policy to simulate the actions and their reward after leaving the tree. MCTS algorithms have been applied with great success to computer Go. To make the two policies fast many enhancements based on online knowledge have been developed. The goal of All Moves as First enhancements is to improve the quality of a reward estimate in the tree policy. In the context of this thesis the, in the field of computer Go very efficient, α-AMAF, Cutoff-AMAF as well as Rapid Action Value Estimation enhancements are implemented in the probabilistic planner PROST. To obtain a better default policy, Move Average Sampling is implemented into PROST and benchmarked against it’s current default policies.

In classical planning the objective is to find a sequence of applicable actions that lead from the initial state to a goal state. In many cases the given problem can be of enormous size. To deal with these cases, a prominent method is to use heuristic search, which uses a heuristic function to evaluate states and can focus on the most promising ones. In addition to applying heuristics, the search algorithm can apply additional pruning techniques that exclude applicable actions in a state because applying them at a later point in the path would result in a path consisting of the same actions but in a different order. The question remains as to how these actions can be selected without generating too much additional work to still be useful for the overall search. In this thesis we implement and evaluate the partition-based path pruning method, proposed by Nissim et al. [1], which tries to decompose the set of all actions into partitions. Based on this decomposition, actions can be pruned with very little additional information. The partition-based pruning method guarantees with some alterations to the A* search algorithm to preserve it’s optimality. The evaluation confirms that in several standard planning domains, the pruning method can reduce the size of the explored state space.

Validating real-time systems is an important and complex task which becomes exponentially harder with increasing sizes of systems. Therefore finding an automated approach to check real-time systems for possible errors is crucial. The behaviour of such real-time systems can be modelled with timed automata. This thesis adapts and implements the under-approximation refinement algorithm developed for search based planners proposed by Heusner et al. to find error states in timed automata via the directed model checking approach. The evaluation compares the algorithm to already existing search methods and shows that a basic under-approximation refinement algorithm yields a competitive search method for directed model checking which is both fast and memory efficient. Additionally we illustrate that with the introduction of some minor alterations the proposed under- approximation refinement algorithm can be further improved.

In dieser Arbeit wird versucht eine Heuristik zu lernen. Damit eine Heuristik erlernbar ist, muss sie über Parameter verfügen, die die Heuristik bestimmen. Eine solche Möglichkeit bieten Potential-Heuristiken und ihre Parameter werden Potentiale genannt. Pattern-Databases können mit vergleichsweise wenig Aufwand Eigenschaften eines Zustandsraumes erkennen und können somit eingesetzt werden als Grundlage um Potentiale zu lernen. Diese Arbeit untersucht zwei verschiedene Ansätze zum Erlernen der Potentiale aufgrund der Information aus Pattern-Databases. In Experimenten werden die beiden Ansätze genauer untersucht und schliesslich mit der FF-Heuristik verglichen.

We consider real-time strategy (RTS) games which have temporal and numerical aspects and pose challenges which have to be solved within limited search time. These games are interesting for AI research because they are more complex than board games. Current AI agents cannot consistently defeat average human players, while even the best players make mistakes we think an AI could avoid. In this thesis, we will focus on StarCraft Brood War. We will introduce a formal definition of the model Churchill and Buro proposed for StarCraft. This allows us to focus on Build Order optimization only. We have implemented a base version of the algorithm Churchill and Buro used for their agent. Using the implementation we are able to find solutions for Build Order Problems in StarCraft Brood War.

Auf dem Gebiet der Handlungsplanung stellt die symbolische Suche eine der erfolgversprechendsten angewandten Techniken dar. Um eine symbolische Suche auf endlichen Zustandsräumen zu implementieren bedarf es einer geeigneten Datenstruktur für logische Formeln. Diese Arbeit erprobt die Nutzung von Sentential Decision Diagrams (SDDs) anstelle der gängigen Binary Decision Diagrams (BDDs) zu diesem Zweck. SDDs sind eine Generalisierung von BDDs. Es wird empirisch getestet wie eine Implementierung der symbolischen Suche mit SDDs im FastDownward-Planer sich mit verschiedenen vtrees unterscheidet. Insbesondere wird die Performance von balancierten vtrees, mit welchen die Stärken von SDDs oft gut zur Geltung kommen, mit rechtsseitig linearen vtrees verglichen, bei welchen sich SDDs wie BDDs verhalten.

Die Frage ob es gültige Sudokus - d.h. Sudokus mit nur einer Lösung - gibt, die nur 16 Vorgaben haben, konnte im Dezember 2011 mithilfe einer erschöpfenden Brute-Force-Methode von McGuire et al. verneint werden. Die Schwierigkeit dieser Aufgabe liegt in dem ausufernden Suchraum des Problems und der dadurch entstehenden Erforderlichkeit einer effizienten Beweisidee sowie schnellerer Algorithmen. In dieser Arbeit wird die Beweismethode von McGuire et al. bestätigt werden und für 2 2 × 2 2 und 3 2 × 3 2 Sudokus in C++ implementiert.

Das Finden eines kürzesten Pfades zwischen zwei Punkten ist ein fundamentales Problem in der Graphentheorie. In der Praxis ist es oft wichtig, den Ressourcenverbrauch für das Ermitteln eines solchen Pfades minimal zu halten, was mithilfe einer komprimierten Pfaddatenbank erreicht werden kann. Im Rahmen dieser Arbeit bestimmen wir drei Verfahren, mit denen eine Pfaddatenbank möglichst platzsparend aufgestellt werden kann, und evaluieren die Effektivität dieser Verfahren anhand von Probleminstanzen verschiedener Grösse und Komplexität.

In planning what we want to do is to get from an initial state into a goal state. A state can be described by a finite number of boolean valued variables. If we want to transition from one state to the other we have to apply an action and this, at least in probabilistic planning, leads to a probability distribution over a set of possible successor states. From each transition the agent gains a reward dependent on the current state and his action. In this setting the growth of the number of possible states is exponential with the number of variables. We assume that the value of these variables is determined for each variable independently in a probabilistic fashion. So these variables influence the number of possible successor states in the same way as they did the state space. In consequence it is almost impossible to obtain an optimal amount of reward approaching this problem with a brute force technique. One way to get past this problem is to abstract the problem and then solve a simplified version of the aforementioned. That’s in general the idea proposed by Boutilier and Dearden [1]. They have introduced a method to create an abstraction which depends on the reward formula and the dependencies contained in the problem. With this idea as a basis we’ll create a heuristic for a trial-based heuristic tree search (THTS) algorithm [5] and a standalone planner using the framework PROST (Keller and Eyerich, 2012). These will then be tested on all the domains of the International Probabilistic Planning Competition (IPPC).

In einer Planungsaufgabe geht es darum einen gegebenen Wertezustand durch sequentielles Anwenden von Aktionen in einen Wertezustand zu überführen, welcher geforderte Zieleigenschaften erfüllt. Beim Lösen von Planungsaufgaben zählt Effizienz. Um Zeit und Speicher zu sparen verwenden viele Planer heuristische Suche. Dabei wird mittels einer Heuristik abgeschätzt, welche Aktion als nächstes angewendet werden soll um möglichst schnell in einen gewünschten Zustand zu gelangen.

In dieser Arbeit geht es darum, die von Haslum vorgeschlagene P m -Kompilierung für Planungsaufgaben zu implementieren und die h max -Heuristik auf dem kompilierten Problem gegen die h m -Heuristik auf dem originalen Problem zu testen. Die Implementation geschieht als Ergänzung zum Fast-Downward-Planungssystem. Die Resultate der Tests zeigen, dass mittels der Kompilierung die Zahl der gelösten Probleme erhöht werden kann. Das Lösen eines kompilierten Problems mit der h max -Heuristik geschieht im allgemeinen mit selbiger Informationstiefe schneller als das Lösen des originalen Problems mit der h m -Heuristik. Diesen Zeitgewinn erkauft man sich mit einem höheren Speicherbedarf.

The objective of classical planning is to find a sequence of actions which begins in a given initial state and ends in a state that satisfies a given goal condition. A popular approach to solve classical planning problems is based on heuristic forward search algorithms. In contrast, regression search algorithms apply actions “backwards” in order to find a plan from a goal state to the initial state. Currently, regression search algorithms are somewhat unpopular, as the generation of partial states in a basic regression search often leads to a significant growth of the explored search space. To tackle this problem, state subsumption is a pruning technique that additionally discards newly generated partial states for which a more general partial state has already been explored.

In this thesis, we discuss and evaluate techniques of regression and state subsumption. In order to evaluate their performance, we have implemented a regression search algorithm for the planning system Fast Downward, supporting both a simple subsumption technique as well as a refined subsumption technique using a trie data structure. The experiments have shown that a basic regression search algorithm generally increases the number of explored states compared to uniform-cost forward search. Regression with pruning based on state subsumption with a trie data structure significantly reduces the number of explored states compared to basic regression.

This thesis discusses the Traveling Tournament Problem and how it can be solved with heuristic search. The Traveling Tournament problem is a sports scheduling problem where one tries to find a schedule for a league that meets certain constraints while minimizing the overall distance traveled by the teams in this league. It is hard to solve for leagues with many teams involved since its complexity grows exponentially in the number of teams. The largest instances solved up to date, are instances with leagues of up to 10 teams.

Previous related work has shown that it is a reasonable approach to solve the Traveling Tournament Problem with an IDA*-based tree search. In this thesis I implemented such a search and extended it with several enhancements to examine whether they improve performance of the search. The heuristic that I used in my implementation is the Independent Lower Bound heuristic. It tries to find lower bounds to the traveling costs of each team in the considered league. With my implementation I was able to solve problem instances with up to 8 teams. The results of my evaluation have mostly been consistent with the expected impact of the implemented enhancements on the overall performance.

One huge topic in Artificial Intelligence is the classical planning. It is the process of finding a plan, therefore a sequence of actions that leads from an initial state to a goal state for a specified problem. In problems with a huge amount of states it is very difficult and time consuming to find a plan. There are different pruning methods that attempt to lower the amount of time needed to find a plan by trying to reduce the number of states to explore. In this work we take a closer look at two of these pruning methods. Both of these methods rely on the last action that led to the current state. The first one is the so called tunnel pruning that is a generalisation of the tunnel macros that are used to solve Sokoban problems. The idea is to find actions that allow a tunnel and then prune all actions that are not in the tunnel of this action. The second method is the partition-based path pruning. In this method all actions are distributed into different partitions. These partitions then can be used to prune actions that do not belong to the current partition.

The evaluation of these two pruning methods show, that they can reduce the number of explored states for some problem domains, however the difference between pruned search and normal search gets smaller when we use heuristic functions. It also shows that the two pruning rules effect different problem domains.

Ziel klassischer Handlungsplanung ist es auf eine möglichst effiziente Weise gegebene Planungsprobleme zu lösen. Die Lösung bzw. der Plan eines Planungsproblems ist eine Sequenz von Operatoren mit denen man von einem Anfangszustand in einen Zielzustand gelangt. Um einen Zielzustand gezielter zu finden, verwenden einige Suchalgorithmen eine zusätzliche Information über den Zustandsraum - die Heuristik. Sie schätzt, ausgehend von einem Zustand den Abstand zum Zielzustand. Demnach wäre es ideal, wenn jeder neue besuchte Zustand einen kleineren heuristischen Wert aufweisen würde als der bisher besuchte Zustand. Es gibt allerdings Suchszenarien bei denen die Heuristik nicht weiterhilft um einem Ziel näher zu kommen. Dies ist insbesondere dann der Fall, wenn sich der heuristische Wert von benachbarten Zuständen nicht ändert. Für die gierige Bestensuche würde das bedeuten, dass die Suche auf Plateaus und somit blind verläuft, weil sich dieser Suchalgorithmus ausschliesslich auf die Heuristik stützt. Algorithmen, die die Heuristik als Wegweiser verwenden, gehören zur Klasse der heuristischen Suchalgorithmen.

In dieser Arbeit geht es darum, in Fällen wie den Plateaus trotzdem eine Orientierung im Zustandsraum zu haben, indem Zustände neben der Heuristik einer weiteren Priorisierung unterliegen. Die hier vorgestellte Methode nutzt Abhängigkeiten zwischen Operatoren aus und erweitert die gierige Bestensuche. Wie stark Operatoren voneinander abhängen, betrachten wir anhand eines Abstandsmasses, welches vor der eigentlichen Suche berechnet wird. Die grundlegende Idee ist, Zustände zu bevorzugen, deren Operatoren im Vorfeld voneinander profitierten. Die Heuristik fungiert hierbei erst im Nachhinein als Tie-Breaker, sodass wir einem vielversprechenden Pfad zunächst folgen können, ohne dass uns die Heuristik an einer anderen, weniger vielversprechenden Stelle suchen lässt.

Die Ergebnisse zeigen, dass unser Ansatz in der reinen Suchzeit je nach Heuristik performanter sein kann, als wenn man sich ausschliesslich auf die Heuristik stützt. Bei sehr informationsreichen Heuristiken kann es jedoch passieren, dass die Suche durch unseren Ansatz eher gestört wird. Zudem werden viele Probleme nicht gelöst, weil die Berechnung der Abstände zu zeitaufwändig ist.

In classical planning, heuristic search is a popular approach to solving problems very efficiently. The objective of planning is to find a sequence of actions that can be applied to a given problem and that leads to a goal state. For this purpose, there are many heuristics. They are often a big help if a problem has a solution, but what happens if a problem does not have one? Which heuristics can help proving unsolvability without exploring the whole state space? How efficient are they? Admissible heuristics can be used for this purpose because they never overestimate the distance to a goal state and are therefore able to safely cut off parts of the search space. This makes it potentially easier to prove unsolvability

In this project we developed a problem generator to automatically create unsolvable problem instances and used those generated instances to see how different admissible heuristics perform on them. We used the Japanese puzzle game Sokoban as the first problem because it has a high complexity but is still easy to understand and to imagine for humans. As second problem, we used a logistical problem called NoMystery because unlike Sokoban it is a resource constrained problem and therefore a good supplement to our experiments. Furthermore, unsolvability occurs rather 'naturally' in these two domains and does not seem forced.

Sokoban is a computer game where each level consists of a two-dimensional grid of fields. There are walls as obstacles, moveable boxes and goal fields. The player controls the warehouse worker (Sokoban in Japanese) to push the boxes to the goal fields. The problem is very complex and that is why Sokoban has become a domain in planning.

Phase transitions mark a sudden change in solvability when traversing through the problem space. They occur in the region of hard instances and have been found for many domains. In this thesis we investigate phase transitions in the Sokoban puzzle. For our investigation we generate and evaluate random instances. We declare the defining parameters for Sokoban and measure their influence on the solvability. We show that phase transitions in the solvability of Sokoban can be found and their occurrence is measured. We attempt to unify the parameters of Sokoban to get a prediction on the solvability and hardness of specific instances.

In planning, we address the problem of automatically finding a sequence of actions that leads from a given initial state to a state that satisfies some goal condition. In satisficing planning, our objective is to find plans with preferably low, but not necessarily the lowest possible costs while keeping in mind our limited resources like time or memory. A prominent approach for satisficing planning is based on heuristic search with inadmissible heuristics. However, depending on the applied heuristic, plans found with heuristic search might be of low quality, and hence, improving the quality of such plans is often desirable. In this thesis, we adapt and apply iterative tunneling search with A* (ITSA*) to planning. ITSA* is an algorithm for plan improvement which has been originally proposed by Furcy et al. for search problems. ITSA* intends to search the local space of a given solution path in order to find "short cuts" which allow us to improve our solution. In this thesis, we provide an implementation and systematic evaluation of this algorithm on the standard IPC benchmarks. Our results show that ITSA* also successfully works in the planning area.

In action planning, greedy best-first search (GBFS) is one of the standard techniques if suboptimal plans are accepted. GBFS uses a heuristic function to guide the search towards a goal state. To achieve generality, in domain-independant planning the heuristic function is generated automatically. A well-known problem of GBFS are search plateaus, i.e., regions in the search space where all states have equal heuristic values. In such regions, heuristic search can degenerate to uninformed search. Hence, techniques to escape from such plateaus are desired to improve the efficiency of the search. A recent approach to avoid plateaus is based on diverse best-first search (DBFS) proposed by Imai and Kishimoto. However, this approach relies on several parameters. This thesis presents an implementation of DBFS into the Fast Downward planner. Furthermore, this thesis presents a systematic evaluation of DBFS for several parameter settings, leading to a better understanding of the impact of the parameter choices to the search performance.

Risk is a popular board game where players conquer each other's countries. In this project, I created an AI that plays Risk and is capable of learning. For each decision it makes, it performs a simple search one step ahead, looking at the outcomes of all possible moves it could make, and picks the most beneficial. It judges the desirability of outcomes by a series of parameters, which are modified after each game using the TD(λ)-Algorithm, allowing the AI to learn.

The Canadian Traveler's Problem ( ctp ) is a path finding problem where due to unfavorable weather, some of the roads are impassable. At the beginning, the agent does not know which roads are traversable and which are not. Instead, it can observe the status of roads adjacent to its current location. We consider the stochastic variant of the problem, where the blocking status of a connection is randomly defined with known probabilities. The goal is to find a policy which minimizes the expected travel costs of the agent.

We discuss several properties of the stochastic ctp and present an efficient way to calculate state probabilities. With the aid of these theoretical results, we introduce an uninformed algorithm to find optimal policies.

Finding optimal solutions for general search problems is a challenging task. A powerful approach for solving such problems is based on heuristic search with pattern database heuristics. In this thesis, we present a domain specific solver for the TopSpin Puzzle problem. This solver is based on the above-mentioned pattern database approach. We investigate several pattern databases, and evaluate them on problem instances of different size.

Merge-and-shrink abstractions are a popular approach to generate abstraction heuristics for planning. The computation of merge-and-shrink abstractions relies on a merging and a shrinking strategy. A recently investigated shrinking strategy is based on using bisimulations. Bisimulations are guaranteed to produce perfect heuristics. In this thesis, we investigate an efficient algorithm proposed by Dovier et al. for computing coarsest bisimulations. The algorithm, however, cannot directly be applied to planning and needs some adjustments. We show how this algorithm can be reduced to work with planning problems. In particular, we show how an edge labelled state space can be translated to a state labelled one and what other changes are necessary for the algorithm to be usable for planning problems. This includes a custom data structure to fulfil all requirements to meet the worst case complexity. Furthermore, the implementation will be evaluated on planning problems from the International Planning Competitions. We will see that the resulting algorithm can often not compete with the currently implemented algorithm in Fast Downward. We discuss the reasons why this is the case and propose possible solutions to resolve this issue.

In order to understand an algorithm, it is always helpful to have a visualization that shows step for step what the algorithm is doing. Under this presumption this Bachelor project will explain and visualize two AI techniques, Constraint Satisfaction Processing and SAT Backbones, using the game Gnomine as an example.

CSP techniques build up a network of constraints and infer information by propagating through a single or several constraints at a time, reducing the domain of the variables in the constraint(s). SAT Backbone Computations find literals in a propositional formula, which are true in every model of the given formula.

By showing how to apply these algorithms on the problem of solving a Gnomine game I hope to give a better insight on the nature of how the chosen algorithms work.

Planning as heuristic search is a powerful approach to solve domain-independent planning problems. An important class of heuristics is based on abstractions of the original planning task. However, abstraction heuristics usually come with loss in precision. The contribution of this thesis is the investigation of constrained abstraction heuristics in general, and the application of this concept to pattern database and merge and shrink abstractions in particular. The idea is to use a subclass of mutexes which represent sets of variable-value-pairs so that only one of these pairs can be true at any given time, to regain some of the precision which is lost in the abstraction without increasing its size. By removing states and operators in the abstraction which conflict with such a mutex, the abstraction is refined and hence, the corresponding abstraction heuristic can get more informed. We have implemented the refinements of these heuristics in the Fast Downward planner and evaluated the different approaches using standard IPC benchmarks. The results show that the concept of constrained abstraction heuristics can improve planning as heuristic search in terms of time and coverage.

A permutation problem considers the task where an initial order of objects (ie, an initial mapping of objects to locations) must be reordered into a given goal order by using permutation operators. Permutation operators are 1:1 mappings of the objects from their locations to (possibly other) locations. An example for permutation problems are the wellknown Rubik's Cube and TopSpin Puzzle. Permutation problems have been a research area for a while, and several methods for solving such problems have been proposed in the last two centuries. Most of these methods focused on finding optimal solutions, causing an exponential runtime in the worst case.

In this work, we consider an algorithm for solving permutation problems that has been originally proposed by M. Furst, J. Hopcroft and E. Luks in 1980. This algorithm has been introduced on a theoretical level within a proof for "Testing Membership and Determining the Order of a Group", but has not been implemented and evaluated on practical problems so far. In contrast to the other abovementioned solving algorithms, it only finds suboptimal solutions, but is guaranteed to run in polynomial time. The basic idea is to iteratively reach subgoals, and then to let them fix when we go further to reach the next goals. We have implemented this algorithm and evaluated it on different models, as the Pancake Problem and the TopSpin Puzzle .

Pattern databases (Culberson & Schaeffer, 1998) or PDBs, have been proven very effective in creating admissible Heuristics for single-agent search, such as the A*-algorithm. Haslum et. al proposed, a hill-climbing algorithm can be used to construct the PDBs, using the canonical heuristic. A different approach would be to change action-costs in the pattern-related abstractions, in order to obtain the admissible heuristic. This the so called Cost-Partitioning.

The aim of this project was to implement a cost-partitioning inside the hill-climbing algorithm by Haslum, and compare the results with the standard way which uses the canonical heuristic.

UCT ("upper confidence bounds applied to trees") is a state-of-the-art algorithm for acting under uncertainty, e.g. in probabilistic environments. In the last years it has been very successfully applied in numerous contexts, including two-player board games like Go and Mancala and stochastic single-agent optimization problems such as path planning under uncertainty and probabilistic action planning.

In this project the UCT algorithm was implemented, adapted and evaluated for the classical arcade game "Ms Pac-Man". The thesis introduces Ms Pac-Man and the UCT algorithm, discusses some critical design decisions for developing a strong UCT-based algorithm for playing Ms Pac-Man, and experimentally evaluates the implementation.

Our Promise
Our Achievements
Our Mission
Proposal Writing
System Development
Paper Writing
Paper Publish
Synopsis Writing
Thesis Writing
Assignments
Survey Paper
Conference Paper
Journal Paper
Empirical Paper
Journal Support
Artificial Intelligence Thesis [List of Top 10 Tools]

Artificial intelligence is the technology where humans’ intelligence is replicated by the supercomputers in the network . Artificial intelligence is often called AI. It is the main branch of computer science to stimulate smart devices with human analytical behaviours.

“This article is completely contented with the interesting concepts related to doing the artificial intelligence thesis”

At the end of this article, you can able to do your thesis by learning the AI concepts ranging from basic to advance . This would be possible by paying your kind attention throughout the article. Generally, artificial intelligence is an emerging technology and it cannot be replaced by any other technology. So it has so many areas to explore. Let us begin this handout with an overview of artificial intelligence.

What is Artificial Intelligence?

Artificial intelligence is imitating human behaviors to perform
They perform utilizing data manipulation, problem-solving, reasoning & learning
They permit human-computer interactions & enhances the processes

This is the overview of artificial intelligence . Researchers in the world are supposing to improve artificial intelligence technology by the way of understanding our emotions and sentiments to respond logically. Here, we thought that it would be nice to list the application areas of artificial intelligence to make you understand . Are you interested to step into the next section? Come on guys let us sail with the flow of the article.

What are the Applications of Artificial Intelligence?

Facial Recognition
Video & Photo Influences
Image Processing , Computer vision & Virtualization
Artificial Inventiveness
Speech Recognition
Handwriting / Text Recognition
Optical Feature Recognition

The above listed are the technical application of artificial intelligence . On the other hand, in day-to-day life, it is also giving their impacts. Some of the examples of artificial intelligence application, in reality , are mentioned below,

Security Surveillance Systems
Demand & Supply Forecasting
Automated Mechanisms
Digital Buyer Support & Robotic Responders
Smart E-mail Classifiers
E-mail, Message & Call Spam Filters

The listed above are the technical & real-time application areas of artificial intelligence in modeling and simulation . On the other hand, artificial intelligence is pillared by some of the other technologies and they are otherwise known as the main themes or topics of AI . Yes, guys, we are going to let you know about the main topics that are involved in artificial intelligence for the ease of your understanding . Shall we move on to that section? Come let’s learn together.

What are the Main Topics in Artificial Intelligence?

Machine Learning
Automated Programs
Computer Vision
Natural Language Processing
Planning & Reasoning
Problem Solving

The above listed are the major topics that get involved in artificial intelligence so far. Moreover, it can be stated that it is the technologies and processes are enriched by the application of artificial intelligence concepts.

As this article is focused on giving the facts about the artificial intelligence thesis , we first wanted to let you know about the list of top 10 frameworks and tools for the ease of your understanding. Our researchers in the institute are very much familiar with the foregoing areas. As proficiency, it reveals our capacities. Let us start to learn about the tools and frameworks with their features for your better understanding .

List of 10 Tools in AI

Torch Description
Torch is a kind of programming language & a scientific computation tool
Torch Features
Large bio network with developer communities
Linear algebra techniques
Numerical optimization procedures
Neural network & energy models
Lua program based C user interfaces
Multi-layer segmenting, normalizing & transferring
Sound N-dimensional ranges
Efficient graphic processing unit
NET Description
net is the machine learning & .NET oriented commercial AI toolkit
It has various numbers of libraries for audio & image preprocessing
It is suggested for the large scale industries as it has the high capacity
NET Features
Audio signals parsing, filtering, saving, and loading
Signal application in spatial domain & frequency
Clustering technique application in arbitrary data inputs
DT, LR & SVM based classification
AutoML Description
AutoML is a machine learning & Google based AI tool
It has dynamic and effective features to handle the massive inputs
AutoML Features
Great performance with high accuracy levels
Effective graphical user interfaces
Fast and easy tool configurations
Lenient operational ML model training
Model developments & evaluations

4.Microsoft CNTK

Microsoft CNTK Description
CNTK refers to the Computational Network Toolkit
CNTK is the Microsoft and deep learning oriented toolkit
Neural networks computational based graphs are described by CNTK
It has similarities among the various devices & graphical processing units
Microsoft CNTK Features
Dynamic adaption regards input formats (audio, text, video) & ideas
Superlative performance & complex task management
Fast & précised training of the models/systems
MXNET is the deep learning-based application framework
It has notable features like lightweight, large scalability & flexibility
It also trains the models in a fast manner
In addition, it is compatible with numerous programming languages
Computer vision & NLP based libraries and tools
SVM deep learning-based compilers are used to test programs by test running
High scalability in supporting the graphic processing units & devices
Multi-host training & GPU is differentiated by the MXNET features
Symbolic & gluon’s eager imperative modes with hybrid front end transitions
They are compatible with the R, Java, C ++, Clojure, Scala & Julia languages
Keras is the neural network & an open-source library
They are capable of running upper on the Tensor flow & Theano
It has an efficient neural network’s API & focused to offer fast empiricism
It is a good suit in both GPU & CPU and RNN & CNN
Effortless debugging & expansions by python codes
Independent modules with complete configurations
Effortless integration of regularization, activation, initialization & optimization
Minimized reasoning loads & curtails the chances for common use case tries
It is mainly designed for humans whereas others are designed for machines
Enhances the user experiences & allows module extensive possibilities
It is a kind of python allied library for estimating numerical expression
Optimizes and defines the multi-dimensional arrays statistical expressions too
They endowing the scientifically based empiricism
In addition, it can integrate linear algebra with its compilers
As well as minimizes the analytical overhead & assimilations
It is possible to minimize the same even over symbolic features variation
Symbol based distinctions by evaluating derivatives
Numpy arrays integration with Theano
Fast evaluation of the expressions by C code creations
Translucent & high-speed graphic processing units
Caffe refers to Convolutional Architecture for Fast Feature Embedding
It is an artificial intelligence-based development framework
It is very thoughtful, modulations & speed in nature
It is scripted in C++ & has python user interfaces
Large developer communities based out from users hub & Github
Animated and innovated architecture & coding-free configurations
Speed processes & implementations in image processing
Concurrent development in the codes and state of models

9.Tensor Flow

Tensor Flow Description
Tensor flow is an open-source AI & machine learning-based framework
It is meant to perform the statistical evaluations
In addition, it has simplified architecture & simple deployment procedures
It is subject to habitual product updates & points issues faced by developers
Tensor Flow Features
Manages and controls networks utilizing allowing developers in few areas
Programming with easy syntaxes & reduces the time for distribution
Permits the users to run various programs simultaneously from other servers
It is compatible with the influential programs & experiments
Resilient output production & simplified deployments

10.Scikit Learn

Scikit learn is a machine learning-based open-source AI toolkit
It has the graphical user interfaces which are based out from python
In addition, it deals with unsupervised & supervised methodologies
It is mainly distributed with the Linux operating systems
It can be used for both academic & commercial purposes
It is presented with the supervised models & methods
It suppresses the visualized attributes dimensionalities
Experimentation of dataset properties & test datasets
Selection of complete attributes & supervised models generation
Unlabeled data can be clustered & cross-validated performance

Before installing the scikit tool consider the following aspects,

Complete 2D or 3D plotting
Structural design & analysis of the data
N-dimensional array or ranges
Emblematic statistics
Scientific computing
Communicative consoles

In the above-listed areas, we have been used some of the terms as acronyms. Hence, we wanted to list out those expansions here for the ease of your understanding.

DT- Decision Trees
SVM- Support Vector Machine
TVM- Tensor Virtual Machine
GPU- Graphics Processing Units
SGD- Stochastic Gradient Descent
BPA- Back Propagation Algorithm
RNN- Recurrent Neural Network
CNN- Convolutional Neural Network

The aforesaid are major and top 10 tools & frameworks aided with artificial intelligence. As of now, we have come up with the overview, application areas, real-time examples , and the top 10 tools and frameworks used for artificial intelligence with brief explanations. So that, we hope you have understood the concepts as of now listed. If you do have any doubts about the above-listed areas you are always welcomed to have our opinions at any time.

Before going to the next phase, we would like to state about our researchers and technicians . In a matter of fact, our technical team does have unique methodologies and techniques for the artificial intelligence base thesis, proposals, projects, and researches . As a point of fact, every work related to the researches is being examined through various quality checks. If you work with us! You might get wonder about our skills. We are a company with 40+ expert researchers who can help the students throughout the research or thesis proposed.

As this article is titled with the artificial intelligence thesis ideas , we felt that it would be the right time to state about the same. Yes, you people guessed right here we are going to mention to you what makes a thesis good. Are you ready to know about that? Come on guys let us we have the section with crisp contents.

What makes Good Thesis Writing?

Clear & succinct statements of the main theme, research purpose & paper argument
Relevant thesis statements & discussions according to the selected topics
Concisely points out to the particular audience
Closure arrangements of statements and basing it for introduction

These are the aspects that should present to make the thesis best comparing to others. Generally, best thesis writing needs experts’ advice. Besides you can have our experts’ pieces of advice in the needed areas and the areas in which you are struggling. We are delighted to guide the students in the fields of artificial intelligence thesis and so on. In this regard, let us discuss how should a thesis be developed for the ease of your understanding.

How Should a Thesis be developed?

Introduction
Literature reviews
Problem findings
Methodologies & techniques
Discussion on outcomes
Absolute conclusions

The above listed are the stages that are should be predetermined before framing your thesis writing . So far, we have discussed the artificial intelligence concepts that are needed to frame the effective thesis. We hope that you would have enjoyed the article completely. Do you interested to explore more about the artificial intelligence thesis? Then here is a suggestion, approach our technical team at any time (24/7).

Let’s inject your innovations & thought processes into the research development eras

MILESTONE 1: Research Proposal

Finalize journal (indexing).

Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

Research Subject Selection

As a doctoral student, subject selection is a big problem. Phdservices.org has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.

Research Topic Selection

We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

Literature Survey Writing

To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Problem Statement

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

Writing Research Proposal

Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

Tools/Plan Approval

We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.

Pseudocode Description

Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.

Develop Proposal Idea

We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.

Comparison/Experiments

We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

Graphs, Results, Analysis Table

We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.

Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.

Collecting Reliable Resources

Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

Writing Rough Draft

We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources

Proofreading & Formatting

We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on

Native English Writing

We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.

Scrutinizing Paper Quality

We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).

Plagiarism Checking

We at phdservices.org is 100% guarantee for original journal paper writing. We never use previously published works.

MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

Paper Submission

We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.

Paper Status Tracking

We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.

Revising Paper Precisely

When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.

Get Accept & e-Proofing

We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.

Publishing Paper

Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link

MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.

Writing Thesis (Preliminary)

We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.

Skimming & Reading

Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.

Fixing Crosscutting Issues

This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.

Organize Thesis Chapters

We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.

Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How PhDservices.org deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

2. Plagiarism-Free

To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

3. Confidential Info

We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.

Technical Info: We never share your technical details to any other scholar since we know the importance of time and resources that are giving us by scholars.
Personal Info: We restricted to access scholars personal details by our experts. Our organization leading team will have your basic and necessary info for scholars.

CONFIDENTIALITY AND PRIVACY OF INFORMATION HELD IS OF VITAL IMPORTANCE AT PHDSERVICES.ORG. WE HONEST FOR ALL CUSTOMERS.

4. Publication

Most of the PhD consultancy services will end their services in Paper Writing, but our PhDservices.org is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.

5. No Duplication

After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.

Client Reviews

I ordered a research proposal in the research area of Wireless Communications and it was as very good as I can catch it.

I had wishes to complete implementation using latest software/tools and I had no idea of where to order it. My friend suggested this place and it delivers what I expect.

It really good platform to get all PhD services and I have used it many times because of reasonable price, best customer services, and high quality.

My colleague recommended this service to me and I’m delighted their services. They guide me a lot and given worthy contents for my research paper.

I’m never disappointed at any kind of service. Till I’m work with professional writers and getting lot of opportunities.

- Christopher

Once I am entered this organization I was just felt relax because lots of my colleagues and family relations were suggested to use this service and I received best thesis writing.

I recommend phdservices.org. They have professional writers for all type of writing (proposal, paper, thesis, assignment) support at affordable price.

You guys did a great job saved more money and time. I will keep working with you and I recommend to others also.

These experts are fast, knowledgeable, and dedicated to work under a short deadline. I had get good conference paper in short span.

Guys! You are the great and real experts for paper writing since it exactly matches with my demand. I will approach again.

I am fully satisfied with thesis writing. Thank you for your faultless service and soon I come back again.

Trusted customer service that you offer for me. I don’t have any cons to say.

I was at the edge of my doctorate graduation since my thesis is totally unconnected chapters. You people did a magic and I get my complete thesis!!!

- Abdul Mohammed

Good family environment with collaboration, and lot of hardworking team who actually share their knowledge by offering PhD Services.

I enjoyed huge when working with PhD services. I was asked several questions about my system development and I had wondered of smooth, dedication and caring.

I had not provided any specific requirements for my proposal work, but you guys are very awesome because I’m received proper proposal. Thank you!

- Bhanuprasad

I was read my entire research proposal and I liked concept suits for my research issues. Thank you so much for your efforts.

- Ghulam Nabi

I am extremely happy with your project development support and source codes are easily understanding and executed.

Hi!!! You guys supported me a lot. Thank you and I am 100% satisfied with publication service.

- Abhimanyu

I had found this as a wonderful platform for scholars so I highly recommend this service to all. I ordered thesis proposal and they covered everything. Thank you so much!!!

Artificial Intelligence in Healthcare Essay

Currently, the healthcare industry faces significant challenges; therefore, technology should be applied to deploy more efficient, precise, and impactful interventions during care delivery. Hamet & Tremblay (2017) explain that patients demand more from healthcare professionals with the evolvement of pay structures and increasing data volumes. Therefore, artificial intelligence (AI) acts as the engine that enhances healthcare settings (Yu et al., 2018). Although machine learning and AI collaboration will transform a wide range of areas such as cloud database, decision-making process, medical processes, mental healthcare radiology, and telehealth, there are challenges associated with its adoption.

The documentation of health records has been hectic because of paperwork. However, Lisowski (2019) explains that it is possible to convert them into electronic documents using natural voice processing and computer vision. Hamet & Tremblay (2017) state that AI transformation will affect mHealth apps and enable personalization of data. As a result, patients can access all health information such as bills, insurance plans, and prescriptions. Moreover, individuals will get essential health records quickly through mobile phones. Additionally, patients will be advised on healthier lifestyle changes considering their particular circumstances (Yu et al., 2018). The decision-making process in healthcare is different and more complicated than in other areas. Reddy et al. (2019) explain that AI can help design patient-friendly platforms that combine professional expertise and personal needs to enhance their relationship and participation during treatment. As a result, every individual will become a valuable partner while conversing with healthcare professionals.

Mental Health Care is among the areas that will significantly benefit from AI. The reason is that artificial intelligence will allow quick tracking of valuable data from sets with massive medical records of the patients (Lisowski, 2019). In addition, the improved AI tools will assist in choosing the best method of treatment and predict the likely results of specific solutions. AI will also help fight depression, the cases of which have rapidly increased over time, by implementing AI into data analysis and considering features such as gender, age, genetics, or the environment. This will encourage doctors to develop the optimal treatment earlier than it is done currently.

The AI application in telehealth can allow patients to video chat or exchange messages to have their conditions diagnosed and receive advice on the best treatment methods and prescriptions. As a result, individuals in areas where specialists are not available and the elderly with commuting challenges will benefit. Machine learning (ML) will play a crucial role in health institutions specialized in radiology (Hamet & Tremblay, 2017). ML will support steps like imaging examination, initial scheduling, or final diagnosis stating. Therefore, ML tools will assist in creating medical reports and treatment plans.

Challenges to the Adoption of AI in Healthcare

Although AI is gaining more popularity in the world, many healthcare organizations are hesitant to implement it. As Shaw et al. (2018) explain, healthcare professionals fear that the innovations will take the jobs previously done by human beings. Additionally, healthcare providers lack sufficient knowledge of implementing AI in their practice. Moreover, the relatively low number of experts with AI technical know-how and good data scientists makes it challenging to adopt it (Shaw et al., 2018). Individuals believe that AI can be biased because it only depends on the available data to decide. Therefore, if the collected information is incorrect, it increases the possibility of making wrong decisions and conclusions.

Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69 , 36-40. Web.

Lisowski, E. (2019). AI will revolutionize healthcare. Medium. Web

Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112 (1), 22-28. Web.

Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019). Artificial intelligence and the implementation challenges. Journal of Medical Internet research, 21 (7), 13659. Web.

Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature biomedical engineering, 2 (10), 719-731. Web.

Chicago (A-D)
Chicago (N-B)

IvyPanda. (2022, August 17). Artificial Intelligence in Healthcare. https://ivypanda.com/essays/artificial-intelligence-in-healthcare/

"Artificial Intelligence in Healthcare." IvyPanda , 17 Aug. 2022, ivypanda.com/essays/artificial-intelligence-in-healthcare/.

IvyPanda . (2022) 'Artificial Intelligence in Healthcare'. 17 August.

IvyPanda . 2022. "Artificial Intelligence in Healthcare." August 17, 2022. https://ivypanda.com/essays/artificial-intelligence-in-healthcare/.

1. IvyPanda . "Artificial Intelligence in Healthcare." August 17, 2022. https://ivypanda.com/essays/artificial-intelligence-in-healthcare/.

Bibliography

IvyPanda . "Artificial Intelligence in Healthcare." August 17, 2022. https://ivypanda.com/essays/artificial-intelligence-in-healthcare/.

Medical Malpractice in the Radiology Field
Radiology in Medicine : Radiation Detection and Measurement Methods
Radiology Technician Argument Essay
Radiology, Physiotherapy and Laboratories Departments
Ethical Issues at the Radiology Department
Historical and Mathematical Perspective on Interventional Radiology
Informed Consent to Patients With Limited English Proficiency in Radiology
The Aspects of Radiology Patient Care
Interventional Radiology and Its Unexplored Uses
Telehealth: A Mind Map and Telehealth Implementation
Information Systems and Changing Organizational Culture
Impacts of EHRS in the Health Sector
Artificial Intelligence in Healthcare and Medicine
MyChart Utilization in the Clinical Setting
The Hospital Telehealth Adoption

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
Tzu Chi Med J
v.32(4); Oct-Dec 2020

The impact of artificial intelligence on human society and bioethics

Michael cheng-tek tai.

Department of Medical Sociology and Social Work, College of Medicine, Chung Shan Medical University, Taichung, Taiwan

Artificial intelligence (AI), known by some as the industrial revolution (IR) 4.0, is going to change not only the way we do things, how we relate to others, but also what we know about ourselves. This article will first examine what AI is, discuss its impact on industrial, social, and economic changes on humankind in the 21 st century, and then propose a set of principles for AI bioethics. The IR1.0, the IR of the 18 th century, impelled a huge social change without directly complicating human relationships. Modern AI, however, has a tremendous impact on how we do things and also the ways we relate to one another. Facing this challenge, new principles of AI bioethics must be considered and developed to provide guidelines for the AI technology to observe so that the world will be benefited by the progress of this new intelligence.

W HAT IS ARTIFICIAL INTELLIGENCE ?

Artificial intelligence (AI) has many different definitions; some see it as the created technology that allows computers and machines to function intelligently. Some see it as the machine that replaces human labor to work for men a more effective and speedier result. Others see it as “a system” with the ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation [ 1 ].

Despite the different definitions, the common understanding of AI is that it is associated with machines and computers to help humankind solve problems and facilitate working processes. In short, it is an intelligence designed by humans and demonstrated by machines. The term AI is used to describe these functions of human-made tool that emulates the “cognitive” abilities of the natural intelligence of human minds [ 2 ].

Along with the rapid development of cybernetic technology in recent years, AI has been seen almost in all our life circles, and some of that may no longer be regarded as AI because it is so common in daily life that we are much used to it such as optical character recognition or the Siri (speech interpretation and recognition interface) of information searching equipment on computer [ 3 ].

D IFFERENT TYPES OF ARTIFICIAL INTELLIGENCE

From the functions and abilities provided by AI, we can distinguish two different types. The first is weak AI, also known as narrow AI that is designed to perform a narrow task, such as facial recognition or Internet Siri search or self-driving car. Many currently existing systems that claim to use “AI” are likely operating as a weak AI focusing on a narrowly defined specific function. Although this weak AI seems to be helpful to human living, there are still some think weak AI could be dangerous because weak AI could cause disruptions in the electric grid or may damage nuclear power plants when malfunctioned.

The new development of the long-term goal of many researchers is to create strong AI or artificial general intelligence (AGI) which is the speculative intelligence of a machine that has the capacity to understand or learn any intelligent task human being can, thus assisting human to unravel the confronted problem. While narrow AI may outperform humans such as playing chess or solving equations, but its effect is still weak. AGI, however, could outperform humans at nearly every cognitive task.

Strong AI is a different perception of AI that it can be programmed to actually be a human mind, to be intelligent in whatever it is commanded to attempt, even to have perception, beliefs and other cognitive capacities that are normally only ascribed to humans [ 4 ].

In summary, we can see these different functions of AI [ 5 , 6 ]:

Automation: What makes a system or process to function automatically
Machine learning and vision: The science of getting a computer to act through deep learning to predict and analyze, and to see through a camera, analog-to-digital conversion and digital signal processing
Natural language processing: The processing of human language by a computer program, such as spam detection and converting instantly a language to another to help humans communicate
Robotics: A field of engineering focusing on the design and manufacturing of cyborgs, the so-called machine man. They are used to perform tasks for human's convenience or something too difficult or dangerous for human to perform and can operate without stopping such as in assembly lines
Self-driving car: Use a combination of computer vision, image recognition amid deep learning to build automated control in a vehicle.

D O HUMAN-BEINGS REALLY NEED ARTIFICIAL INTELLIGENCE ?

Is AI really needed in human society? It depends. If human opts for a faster and effective way to complete their work and to work constantly without taking a break, yes, it is. However if humankind is satisfied with a natural way of living without excessive desires to conquer the order of nature, it is not. History tells us that human is always looking for something faster, easier, more effective, and convenient to finish the task they work on; therefore, the pressure for further development motivates humankind to look for a new and better way of doing things. Humankind as the homo-sapiens discovered that tools could facilitate many hardships for daily livings and through tools they invented, human could complete the work better, faster, smarter and more effectively. The invention to create new things becomes the incentive of human progress. We enjoy a much easier and more leisurely life today all because of the contribution of technology. The human society has been using the tools since the beginning of civilization, and human progress depends on it. The human kind living in the 21 st century did not have to work as hard as their forefathers in previous times because they have new machines to work for them. It is all good and should be all right for these AI but a warning came in early 20 th century as the human-technology kept developing that Aldous Huxley warned in his book Brave New World that human might step into a world in which we are creating a monster or a super human with the development of genetic technology.

Besides, up-to-dated AI is breaking into healthcare industry too by assisting doctors to diagnose, finding the sources of diseases, suggesting various ways of treatment performing surgery and also predicting if the illness is life-threatening [ 7 ]. A recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot to perform soft-tissue surgery, stitch together a pig's bowel, and the robot finished the job better than a human surgeon, the team claimed [ 8 , 9 ]. It demonstrates robotically-assisted surgery can overcome the limitations of pre-existing minimally-invasive surgical procedures and to enhance the capacities of surgeons performing open surgery.

Above all, we see the high-profile examples of AI including autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays…etc. All these have made human life much easier and convenient that we are so used to them and take them for granted. AI has become indispensable, although it is not absolutely needed without it our world will be in chaos in many ways today.

T HE IMPACT OF ARTIFICIAL INTELLIGENCE ON HUMAN SOCIETY

Negative impact.

Questions have been asked: With the progressive development of AI, human labor will no longer be needed as everything can be done mechanically. Will humans become lazier and eventually degrade to the stage that we return to our primitive form of being? The process of evolution takes eons to develop, so we will not notice the backsliding of humankind. However how about if the AI becomes so powerful that it can program itself to be in charge and disobey the order given by its master, the humankind?

Let us see the negative impact the AI will have on human society [ 10 , 11 ]:

A huge social change that disrupts the way we live in the human community will occur. Humankind has to be industrious to make their living, but with the service of AI, we can just program the machine to do a thing for us without even lifting a tool. Human closeness will be gradually diminishing as AI will replace the need for people to meet face to face for idea exchange. AI will stand in between people as the personal gathering will no longer be needed for communication
Unemployment is the next because many works will be replaced by machinery. Today, many automobile assembly lines have been filled with machineries and robots, forcing traditional workers to lose their jobs. Even in supermarket, the store clerks will not be needed anymore as the digital device can take over human labor
Wealth inequality will be created as the investors of AI will take up the major share of the earnings. The gap between the rich and the poor will be widened. The so-called “M” shape wealth distribution will be more obvious
New issues surface not only in a social sense but also in AI itself as the AI being trained and learned how to operate the given task can eventually take off to the stage that human has no control, thus creating un-anticipated problems and consequences. It refers to AI's capacity after being loaded with all needed algorithm may automatically function on its own course ignoring the command given by the human controller
The human masters who create AI may invent something that is racial bias or egocentrically oriented to harm certain people or things. For instance, the United Nations has voted to limit the spread of nucleus power in fear of its indiscriminative use to destroying humankind or targeting on certain races or region to achieve the goal of domination. AI is possible to target certain race or some programmed objects to accomplish the command of destruction by the programmers, thus creating world disaster.

P OSITIVE IMPACT

There are, however, many positive impacts on humans as well, especially in the field of healthcare. AI gives computers the capacity to learn, reason, and apply logic. Scientists, medical researchers, clinicians, mathematicians, and engineers, when working together, can design an AI that is aimed at medical diagnosis and treatments, thus offering reliable and safe systems of health-care delivery. As health professors and medical researchers endeavor to find new and efficient ways of treating diseases, not only the digital computer can assist in analyzing, robotic systems can also be created to do some delicate medical procedures with precision. Here, we see the contribution of AI to health care [ 7 , 11 ]:

Fast and accurate diagnostics

IBM's Watson computer has been used to diagnose with the fascinating result. Loading the data to the computer will instantly get AI's diagnosis. AI can also provide various ways of treatment for physicians to consider. The procedure is something like this: To load the digital results of physical examination to the computer that will consider all possibilities and automatically diagnose whether or not the patient suffers from some deficiencies and illness and even suggest various kinds of available treatment.

Socially therapeutic robots

Pets are recommended to senior citizens to ease their tension and reduce blood pressure, anxiety, loneliness, and increase social interaction. Now cyborgs have been suggested to accompany those lonely old folks, even to help do some house chores. Therapeutic robots and the socially assistive robot technology help improve the quality of life for seniors and physically challenged [ 12 ].

Reduce errors related to human fatigue

Human error at workforce is inevitable and often costly, the greater the level of fatigue, the higher the risk of errors occurring. Al technology, however, does not suffer from fatigue or emotional distraction. It saves errors and can accomplish the duty faster and more accurately.

Artificial intelligence-based surgical contribution

AI-based surgical procedures have been available for people to choose. Although this AI still needs to be operated by the health professionals, it can complete the work with less damage to the body. The da Vinci surgical system, a robotic technology allowing surgeons to perform minimally invasive procedures, is available in most of the hospitals now. These systems enable a degree of precision and accuracy far greater than the procedures done manually. The less invasive the surgery, the less trauma it will occur and less blood loss, less anxiety of the patients.

Improved radiology

The first computed tomography scanners were introduced in 1971. The first magnetic resonance imaging (MRI) scan of the human body took place in 1977. By the early 2000s, cardiac MRI, body MRI, and fetal imaging, became routine. The search continues for new algorithms to detect specific diseases as well as to analyze the results of scans [ 9 ]. All those are the contribution of the technology of AI.

Virtual presence

The virtual presence technology can enable a distant diagnosis of the diseases. The patient does not have to leave his/her bed but using a remote presence robot, doctors can check the patients without actually being there. Health professionals can move around and interact almost as effectively as if they were present. This allows specialists to assist patients who are unable to travel.

S OME CAUTIONS TO BE REMINDED

Despite all the positive promises that AI provides, human experts, however, are still essential and necessary to design, program, and operate the AI from any unpredictable error from occurring. Beth Kindig, a San Francisco-based technology analyst with more than a decade of experience in analyzing private and public technology companies, published a free newsletter indicating that although AI has a potential promise for better medical diagnosis, human experts are still needed to avoid the misclassification of unknown diseases because AI is not omnipotent to solve all problems for human kinds. There are times when AI meets an impasse, and to carry on its mission, it may just proceed indiscriminately, ending in creating more problems. Thus vigilant watch of AI's function cannot be neglected. This reminder is known as physician-in-the-loop [ 13 ].

The question of an ethical AI consequently was brought up by Elizabeth Gibney in her article published in Nature to caution any bias and possible societal harm [ 14 ]. The Neural Information processing Systems (NeurIPS) conference in Vancouver Canada in 2020 brought up the ethical controversies of the application of AI technology, such as in predictive policing or facial recognition, that due to bias algorithms can result in hurting the vulnerable population [ 14 ]. For instance, the NeurIPS can be programmed to target certain race or decree as the probable suspect of crime or trouble makers.

T HE CHALLENGE OF ARTIFICIAL INTELLIGENCE TO BIOETHICS

Artificial intelligence ethics must be developed.

Bioethics is a discipline that focuses on the relationship among living beings. Bioethics accentuates the good and the right in biospheres and can be categorized into at least three areas, the bioethics in health settings that is the relationship between physicians and patients, the bioethics in social settings that is the relationship among humankind and the bioethics in environmental settings that is the relationship between man and nature including animal ethics, land ethics, ecological ethics…etc. All these are concerned about relationships within and among natural existences.

As AI arises, human has a new challenge in terms of establishing a relationship toward something that is not natural in its own right. Bioethics normally discusses the relationship within natural existences, either humankind or his environment, that are parts of natural phenomena. But now men have to deal with something that is human-made, artificial and unnatural, namely AI. Human has created many things yet never has human had to think of how to ethically relate to his own creation. AI by itself is without feeling or personality. AI engineers have realized the importance of giving the AI ability to discern so that it will avoid any deviated activities causing unintended harm. From this perspective, we understand that AI can have a negative impact on humans and society; thus, a bioethics of AI becomes important to make sure that AI will not take off on its own by deviating from its originally designated purpose.

Stephen Hawking warned early in 2014 that the development of full AI could spell the end of the human race. He said that once humans develop AI, it may take off on its own and redesign itself at an ever-increasing rate [ 15 ]. Humans, who are limited by slow biological evolution, could not compete and would be superseded. In his book Superintelligence, Nick Bostrom gives an argument that AI will pose a threat to humankind. He argues that sufficiently intelligent AI can exhibit convergent behavior such as acquiring resources or protecting itself from being shut down, and it might harm humanity [ 16 ].

The question is–do we have to think of bioethics for the human's own created product that bears no bio-vitality? Can a machine have a mind, consciousness, and mental state in exactly the same sense that human beings do? Can a machine be sentient and thus deserve certain rights? Can a machine intentionally cause harm? Regulations must be contemplated as a bioethical mandate for AI production.

Studies have shown that AI can reflect the very prejudices humans have tried to overcome. As AI becomes “truly ubiquitous,” it has a tremendous potential to positively impact all manner of life, from industry to employment to health care and even security. Addressing the risks associated with the technology, Janosch Delcker, Politico Europe's AI correspondent, said: “I don't think AI will ever be free of bias, at least not as long as we stick to machine learning as we know it today,”…. “What's crucially important, I believe, is to recognize that those biases exist and that policymakers try to mitigate them” [ 17 ]. The High-Level Expert Group on AI of the European Union presented Ethics Guidelines for Trustworthy AI in 2019 that suggested AI systems must be accountable, explainable, and unbiased. Three emphases are given:

Lawful-respecting all applicable laws and regulations
Ethical-respecting ethical principles and values
Robust-being adaptive, reliable, fair, and trustworthy from a technical perspective while taking into account its social environment [ 18 ].

Seven requirements are recommended [ 18 ]:

AI should not trample on human autonomy. People should not be manipulated or coerced by AI systems, and humans should be able to intervene or oversee every decision that the software makes
AI should be secure and accurate. It should not be easily compromised by external attacks, and it should be reasonably reliable
Personal data collected by AI systems should be secure and private. It should not be accessible to just anyone, and it should not be easily stolen
Data and algorithms used to create an AI system should be accessible, and the decisions made by the software should be “understood and traced by human beings.” In other words, operators should be able to explain the decisions their AI systems make
Services provided by AI should be available to all, regardless of age, gender, race, or other characteristics. Similarly, systems should not be biased along these lines
AI systems should be sustainable (i.e., they should be ecologically responsible) and “enhance positive social change”
AI systems should be auditable and covered by existing protections for corporate whistleblowers. The negative impacts of systems should be acknowledged and reported in advance.

From these guidelines, we can suggest that future AI must be equipped with human sensibility or “AI humanities.” To accomplish this, AI researchers, manufacturers, and all industries must bear in mind that technology is to serve not to manipulate humans and his society. Bostrom and Judkowsky listed responsibility, transparency, auditability, incorruptibility, and predictability [ 19 ] as criteria for the computerized society to think about.

S UGGESTED PRINCIPLES FOR ARTIFICIAL INTELLIGENCE BIOETHICS

Nathan Strout, a reporter at Space and Intelligence System at Easter University, USA, reported just recently that the intelligence community is developing its own AI ethics. The Pentagon made announced in February 2020 that it is in the process of adopting principles for using AI as the guidelines for the department to follow while developing new AI tools and AI-enabled technologies. Ben Huebner, chief of the Office of Director of National Intelligence's Civil Liberties, Privacy, and Transparency Office, said that “We're going to need to ensure that we have transparency and accountability in these structures as we use them. They have to be secure and resilient” [ 20 ]. Two themes have been suggested for the AI community to think more about: Explainability and interpretability. Explainability is the concept of understanding how the analytic works, while interpretability is being able to understand a particular result produced by an analytic [ 20 ].

All the principles suggested by scholars for AI bioethics are well-brought-up. I gather from different bioethical principles in all the related fields of bioethics to suggest four principles here for consideration to guide the future development of the AI technology. We however must bear in mind that the main attention should still be placed on human because AI after all has been designed and manufactured by human. AI proceeds to its work according to its algorithm. AI itself cannot empathize nor have the ability to discern good from evil and may commit mistakes in processes. All the ethical quality of AI depends on the human designers; therefore, it is an AI bioethics and at the same time, a trans-bioethics that abridge human and material worlds. Here are the principles:

Beneficence: Beneficence means doing good, and here it refers to the purpose and functions of AI should benefit the whole human life, society and universe. Any AI that will perform any destructive work on bio-universe, including all life forms, must be avoided and forbidden. The AI scientists must understand that reason of developing this technology has no other purpose but to benefit human society as a whole not for any individual personal gain. It should be altruistic, not egocentric in nature
Value-upholding: This refers to AI's congruence to social values, in other words, universal values that govern the order of the natural world must be observed. AI cannot elevate to the height above social and moral norms and must be bias-free. The scientific and technological developments must be for the enhancement of human well-being that is the chief value AI must hold dearly as it progresses further
Lucidity: AI must be transparent without hiding any secret agenda. It has to be easily comprehensible, detectable, incorruptible, and perceivable. AI technology should be made available for public auditing, testing and review, and subject to accountability standards … In high-stakes settings like diagnosing cancer from radiologic images, an algorithm that can't “explain its work” may pose an unacceptable risk. Thus, explainability and interpretability are absolutely required
Accountability: AI designers and developers must bear in mind they carry a heavy responsibility on their shoulders of the outcome and impact of AI on whole human society and the universe. They must be accountable for whatever they manufacture and create.

C ONCLUSION

AI is here to stay in our world and we must try to enforce the AI bioethics of beneficence, value upholding, lucidity and accountability. Since AI is without a soul as it is, its bioethics must be transcendental to bridge the shortcoming of AI's inability to empathize. AI is a reality of the world. We must take note of what Joseph Weizenbaum, a pioneer of AI, said that we must not let computers make important decisions for us because AI as a machine will never possess human qualities such as compassion and wisdom to morally discern and judge [ 10 ]. Bioethics is not a matter of calculation but a process of conscientization. Although AI designers can up-load all information, data, and programmed to AI to function as a human being, it is still a machine and a tool. AI will always remain as AI without having authentic human feelings and the capacity to commiserate. Therefore, AI technology must be progressed with extreme caution. As Von der Leyen said in White Paper on AI – A European approach to excellence and trust : “AI must serve people, and therefore, AI must always comply with people's rights…. High-risk AI. That potentially interferes with people's rights has to be tested and certified before it reaches our single market” [ 21 ].

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

R EFERENCES

Resources Home 🏠
Try SciSpace Copilot
Search research papers
Add Copilot Extension
Try AI Detector
Try Paraphraser
Try Citation Generator
April Papers
June Papers
July Papers

AI for thesis writing — Unveiling 7 best AI tools

Writing a thesis is akin to piecing together a complex puzzle. Each research paper, every data point, and all the hours spent reading and analyzing contribute to this monumental task.

For many students, this journey is a relentless pursuit of knowledge, often marked by sleepless nights and tight deadlines.

Here, the potential of AI for writing a thesis or research papers becomes clear: artificial intelligence can step in, not to take over but to assist and guide.

Far from being just a trendy term, AI is revolutionizing academic research, offering tools that can make the task of thesis writing more manageable, more precise, and a little less overwhelming.

In this article, we’ll discuss the impact of AI on academic writing process, and articulate the best AI tools for thesis writing to enhance your thesis writing process.

The Impact of AI on Thesis Writing

Artificial Intelligence offers a supportive hand in thesis writing, adeptly navigating vast datasets, suggesting enhancements in writing, and refining the narrative.

With the integration of AI writing assistant, instead of requiring you to manually sift through endless articles, AI tools can spotlight the most pertinent pieces in mere moments. Need clarity or the right phrasing? AI-driven writing assistants are there, offering real-time feedback, ensuring your work is both articulative and academically sound.

AI tools for thesis writing harness Natural Language Processing (NLP) to generate content, check grammar, and assist in literature reviews. Simultaneously, Machine Learning (ML) techniques enable data analysis, provide personalized research recommendations, and aid in proper citation.

And for the detailed tasks of academic formatting and referencing? AI streamlines it all, ensuring your thesis meets the highest academic standards.

However, understanding AI's role is pivotal. It's a supportive tool, not the primary author. Your thesis remains a testament to your unique perspective and voice.

AI for writing thesis is there to amplify that voice, ensuring it's heard clearly and effectively.

How AI tools supplement your thesis writing

AI tools have emerged as invaluable allies for scholars. With just a few clicks, these advanced platforms can streamline various aspects of thesis writing, from data analysis to literature review.

Let's explore how an AI tool can supplement and transform your thesis writing style and process.

Efficient literature review : AI tools can quickly scan and summarize vast amounts of literature, making the process of literature review more efficient. Instead of spending countless hours reading through papers, researchers can get concise summaries and insights, allowing them to focus on relevant content.

Enhanced data analysis : AI algorithms can process and analyze large datasets with ease, identifying patterns, trends, and correlations that might be difficult or time-consuming for humans to detect. This capability is especially valuable in fields with massive datasets, like genomics or social sciences.

Improved writing quality : AI-powered writing assistants can provide real-time feedback on grammar, style, and coherence. They can suggest improvements, ensuring that the final draft of a research paper or thesis is of high quality.

Plagiarism detection : AI tools can scan vast databases of academic content to ensure that a researcher's work is original and free from unintentional plagiarism .

Automated citations : Managing and formatting citations is a tedious aspect of academic writing. AI citation generators can automatically format citations according to specific journal or conference standards, reducing the chances of errors.

Personalized research recommendations : AI tools can analyze a researcher's past work and reading habits to recommend relevant papers and articles, ensuring that they stay updated with the latest in their field.

Interactive data visualization : AI can assist in creating dynamic and interactive visualizations, making it easier for researchers to present their findings in a more engaging manner.

Top 7 AI Tools for Thesis Writing

The academic field is brimming with AI tools tailored for academic paper writing. Here's a glimpse into some of the most popular and effective ones.

Here we'll talk about some of the best ai writing tools, expanding on their major uses, benefits, and reasons to consider them.

If you've ever been bogged down by the minutiae of formatting or are unsure about specific academic standards, Typeset is a lifesaver.

Typeset specializes in formatting, ensuring academic papers align with various journal and conference standards.

It automates the intricate process of academic formatting, saving you from the manual hassle and potential errors, inflating your writing experience.

An AI-driven writing assistant, Wisio elevates the quality of your thesis content. It goes beyond grammar checks, offering style suggestions tailored to academic writing.

This ensures your thesis is both grammatically correct and maintains a scholarly tone. For moments of doubt or when maintaining a consistent style becomes challenging, Wisio acts as your personal editor, providing real-time feedback.

Known for its ability to generate and refine thesis content using AI algorithms, Texti ensures logical and coherent content flow according to the academic guidelines.

When faced with writer's block or a blank page, Texti can jumpstart your thesis writing process, aiding in drafting or refining content.

JustDone is an AI for thesis writing and content creation. It offers a straightforward three-step process for generating content, from choosing a template to customizing details and enjoying the final output.

JustDone AI can generate thesis drafts based on the input provided by you. This can be particularly useful for getting started or overcoming writer's block.

This platform can refine and enhance the editing process, ensuring it aligns with academic standards and is free from common errors. Moreover, it can process and analyze data, helping researchers identify patterns, trends, and insights that might be crucial for their thesis.

Tailored for academic writing, Writefull offers style suggestions to ensure your content maintains a scholarly tone.

This AI for thesis writing provides feedback on your language use, suggesting improvements in grammar, vocabulary, and structure . Moreover, it compares your written content against a vast database of academic texts. This helps in ensuring that your writing is in line with academic standards.

Isaac Editor

For those seeking an all-in-one solution for writing, editing, and refining, Isaac Editor offers a comprehensive platform.

Combining traditional text editor features with AI, Isaac Editor streamlines the writing process. It's an all-in-one solution for writing, editing, and refining, ensuring your content is of the highest quality.

PaperPal , an AI-powered personal writing assistant, enhances academic writing skills, particularly for PhD thesis writing and English editing.

This AI for thesis writing offers comprehensive grammar, spelling, punctuation, and readability suggestions, along with detailed English writing tips.

It offers grammar checks, providing insights on rephrasing sentences, improving article structure, and other edits to refine academic writing.

The platform also offers tools like "Paperpal for Word" and "Paperpal for Web" to provide real-time editing suggestions, and "Paperpal for Manuscript" for a thorough check of completed articles or theses.

Is it ethical to use AI for thesis writing?

The AI for writing thesis has ignited discussions on authenticity. While AI tools offer unparalleled assistance, it's vital to maintain originality and not become overly reliant. Research thrives on unique contributions, and AI should be a supportive tool, not a replacement.

The key question: Can a thesis, significantly aided by AI, still be viewed as an original piece of work?

AI tools can simplify research, offer grammar corrections, and even produce content. However, there's a fine line between using AI as a helpful tool and becoming overly dependent on it.

In essence, while AI offers numerous advantages for thesis writing, it's crucial to use it judiciously. AI should complement human effort, not replace it. The challenge is to strike the right balance, ensuring genuine research contributions while leveraging AI's capabilities.

Wrapping Up

Nowadays, it's evident that AI tools are not just fleeting trends but pivotal game-changers.

They're reshaping how we approach, structure, and refine our theses, making the process more efficient and the output more impactful. But amidst this technological revolution, it's essential to remember the heart of any thesis: the researcher's unique voice and perspective .

AI tools are here to amplify that voice, not overshadow it. They're guiding you through the vast sea of information, ensuring our research stands out and resonates.

Try these tools out and let us know what worked for you the best.

Love using SciSpace tools? Enjoy discounts! Use SR40 (40% off yearly) and SR20 (20% off monthly). Claim yours here 👉 SciSpace Premium

Frequently Asked Questions

Yes, you can use AI to assist in writing your thesis. AI tools can help streamline various aspects of the writing process, such as data analysis, literature review, grammar checks, and content refinement.

However, it's essential to use AI as a supportive tool and not a replacement for original research and critical thinking. Your thesis should reflect your unique perspective and voice.

Yes, there are AI tools designed to assist in writing research papers. These tools can generate content, suggest improvements, help with formatting, and even provide real-time feedback on grammar and coherence.

Examples include Typeset, JustDone, Writefull, and Texti. However, while they can aid the process, the primary research, analysis, and conclusions should come from the researcher.

The "best" AI for writing papers depends on your specific needs. For content generation and refinement, Texti is a strong contender.

For grammar checks and style suggestions tailored to academic writing, Writefull is highly recommended. JustDone offers a user-friendly interface for content creation. It's advisable to explore different tools and choose one that aligns with your requirements.

To use AI for writing your thesis:

1. Identify the areas where you need assistance, such as literature review, data analysis, content generation, or grammar checks.

2. Choose an AI tool tailored for academic writing, like Typeset, JustDone, Texti, or Writefull.

3. Integrate the tool into your writing process. This could mean using it as a browser extension, a standalone application, or a plugin for your word processor.

4. As you write or review content, use the AI tool for real-time feedback, suggestions, or content generation.

5. Always review and critically assess the suggestions or content provided by the AI to ensure it aligns with your research goals and maintains academic integrity.

What is a thesis | A Complete Guide with Examples

How to Craft Effective Thesis Statements With AI Writing Tools?

April 7, 2024 Updated

How-to-Craft-Effective-Thesis-Statements-With-AI-Writing-Tools

As an academic writer, I’ve found that the thesis statement is a critical element of any paper. It sets the tone and direction for my writing. Recently, I’ve been incorporating best AI writing tools to write thesis into my process, and they’ve significantly transformed how I approach thesis statements.

In this article, I’ll share my experience with some of the best AI writing tools , highlighting how to craft effective thesis statements with AI writing tools. Let’s get into it.

How to Craft Effective Thesis Statements With AI Writing Tools: Step-by-Step Guide

Here, I’ll share my step-by-step approach and teach you how to craft effective thesis statements with AI writing tools. This method has consistently improved both the efficiency and effectiveness of my academic writing.

Step 1: Identifying the Topic

The first step in thesis writing is identifying a relevant and engaging topic. AI tools such as OpenAI’s GPT-4 are invaluable in this stage, offering up-to-date suggestions on trending and significant topics.

These tools analyze current research and discussions in various fields, providing me with a broad range of potential topics that are both contemporary and academically relevant.

Step 2: Narrowing the Focus

Once a topic is chosen, the next challenge is to narrow it down to a specific aspect that is both manageable and significant.

AI tools integrated with databases like Google Scholar or JSTOR aid in this process by analyzing large volumes of data and research papers.

This helps in focusing on a particular aspect or angle of the chosen topic, ensuring that the thesis is both specific and substantive.

Step 3: Formulating the Argument

Formulating a clear and debatable thesis statement is a critical step. AI-powered tools like Grammarly and Hemingway Editor are useful here.

They offer linguistic analysis, suggesting ways to construct a strong argument that is both clear and persuasive.

These right AI writing tools analyze sentence structure, word choice, and overall readability, ensuring that the thesis statement is cogent and impactful.

Step 4: Refining the Thesis Statement

Refinement is key to crafting an effective thesis statement. AI tools such as ProWritingAid provide suggestions on improving the clarity and conciseness of the statement.

They help in fine-tuning the language, ensuring that the thesis statement is well-phrased, impactful, and devoid of any ambiguity or redundancy.

Step 5: Seeking Feedback

Gathering feedback on the thesis statement is crucial. Platforms like Scribbr use AI algorithms to provide constructive feedback on the strength, coherence, and clarity of the thesis statement.

They offer insights into how the statement can be improved, making it more robust and compelling.

Step 6: Final Review

Finally, ensuring the originality of the thesis statement is paramount. Tools like Turnitin are essential in this final stage.

They check for originality and uniqueness, ensuring that the thesis statement is free from unintentional plagiarism and stands out in the academic discourse.

What Are the Essentials of a Thesis Statement?

In my writing, I ensure that my thesis statement is clear, concise, and well-defined, demonstrating how to craft effective thesis statements with AI writing tools. It’s the guiding light for the reader, steering them through my argument or analysis.

How-to-craft-effective-thesis statement-with-ai-writing-tools

The key elements I focus on in a strong thesis statement include:

Clarity and Conciseness

A thesis statement is the backbone of any well-written academic paper, providing a clear and concise summary of the argument or analysis that follows. It’s essential that this statement is devoid of complex jargon and unnecessarily long sentences.

Clarity ensures immediate comprehension by the reader, while conciseness prevents dilution of the core argument, keeping the reader’s attention focused.

Specificity and Focus

The effectiveness of a thesis statement largely depends on its ability to narrow down a broad topic into a specific, focused argument.

This specificity allows for a detailed and in-depth exploration of the subject matter, preventing the paper from veering off into too general or unrelated discussions.

A focused thesis guides the direction of research and writing, ensuring that every element of the paper contributes towards exploring this central argument.

Arguable and Defensible

An impactful thesis statement is one that presents a clear argument or perspective, which is not only open to discussion but can also be supported with concrete evidence.

This argumentative nature invites critical thinking and engagement from the reader, prompting them to consider the topic from the writer’s perspective and anticipate the evidence that will be presented in support of this argument.

Originality and Insight

In academic writing, a thesis statement should not merely state a fact or a universally accepted truth. Instead, it should offer an original perspective or a novel approach to the topic.

This originality is what contributes to the broader academic dialogue, providing new insights or challenging existing ones.

The thesis should make a unique contribution to the topic, showcasing the writer’s deep understanding and personal interpretation of the subject. The best AI writing tools for academic writing can help ensure originality in your thesis statement.

Alignment with the Paper

A thesis statement must be in harmony with the rest of the paper. Every paragraph, argument, and piece of evidence presented in the paper should directly support and reinforce the thesis statement.

This alignment ensures a cohesive and coherent structure, where all elements of the paper work synergistically to argue or analyze the central claim.

How AI Writing Tools Assist in Refining and Rewriting Thesis Statements

In my experience, artificial intelligence tools are excellent at offering alternative phrasings, checking consistency with the overall content, and improving clarity, embodying how to craft effective thesis statements with AI writing tools. They suggest various ways to express the thesis and ensure it aligns with the rest of my content.

Suggesting Variations

One of the primary ways AI tools assist in thesis writing is by suggesting multiple variations of a thesis statement.

These suggestions provide different ways of expressing the same idea, helping to find the most effective and impactful phrasing.

Checking Consistency

AI tools are highly efficient in ensuring that the thesis statement aligns with the overall content and tone of the paper.

They analyze the entire document to ensure that every part of the paper supports and reinforces the thesis, maintaining a cohesive argument throughout.

Improving Clarity

AI suggestions are instrumental in enhancing the clarity of the thesis statement. They help rephrase complex or ambiguous statements into clearer, more understandable language. This improvement in clarity is crucial for engaging and retaining the reader’s interest.

Enhancing Persuasiveness

AI tools are adept at suggesting more persuasive language and stronger arguments, thereby making the thesis more compelling.

They analyze the persuasive elements of language, such as word choice and sentence structure, to enhance the argumentative power of the thesis statement.

Detecting Redundancies

AI tools efficiently identify and eliminate redundant phrases or arguments within the thesis statement. This streamlining ensures that the statement is concise and focused, enhancing its overall impact.

Offering Customized Suggestions

Based on the style, tone, and topic of the paper, AI tools provide tailored advice to make the thesis more effective.

These customized suggestions are particularly helpful in ensuring that the thesis statement is well-suited to the specific requirements and expectations of the paper’s intended audience.

Incorporating AI Tools: A Comparative Insight

Comparing AI tools with traditional methods, I find that AI significantly enhances efficiency in idea generation, multilingual content creation , and real-time suggestions, making the process of crafting a thesis statement less time-consuming and more effective.

Speed of Research

One of the most significant advantages of using AI tools in thesis writing is the speed at which research can be conducted.

AI tools provide quick and easy access to a vast array of datasets and research materials, a process that would take significantly longer using traditional research methods.

Idea Generation

Compared to traditional brainstorming methods, AI tools offer immediate, diverse, and often more creative ideas for thesis statements.

These tools can analyze existing literature and trends to suggest unique angles and perspectives that might not be immediately apparent through conventional brainstorming.

Language and Style

In terms of language and style, AI tools offer a level of refinement and sophistication that surpasses traditional manual proofreading and editing.

They analyze the thesis statement for language use, style, tone, and readability, ensuring that it meets high standards of academic writing.

Consistency Checking

AI tools are more efficient and accurate in checking for consistency in the thesis statement and throughout the paper.

They ensure that every part of the paper aligns with and supports the central thesis, which can be more challenging to achieve with manual reviews.

Accessibility of Resources

When it comes to accessing research materials and references, AI tools provide a broader and more comprehensive range of resources than traditional library-based research.

They offer access to the latest studies, articles, and data from various disciplines, making the research process more efficient and thorough.

Plagiarism Detection

AI-powered plagiarism checkers offer a level of thoroughness and accuracy in detecting plagiarism that is difficult to achieve through manual methods.

They scan a wide range of sources, including academic papers and online content, ensuring that the thesis statement is original and free from unintentional plagiarism.

How Do Different Paper Types Influence Thesis Formation?

The nature of the thesis statement depends on whether the paper is analytical, expository, or argumentative. Understanding how to craft effective thesis statements with AI writing tools can greatly assist in tailoring your thesis to fit these specific types of papers effectively.

I use AI tools tailored to understand these differences, which offer appropriate suggestions based on the paper type.

Analytical Papers

In analytical papers, the thesis statement should dissect an idea or issue into its essential components, providing a clear and focused analysis of each part.

The thesis must guide the structure of the analysis, determining the key aspects or elements that will be examined in detail.

Expository Papers

For expository papers, the thesis should aim to explain or elucidate a particular concept or idea.

It should be informative and educational, providing clarity and insight into the subject.

A thesis in expository papers often outlines the aspects that will be explored to offer a comprehensive understanding of the topic.

Argumentative Papers

In argumentative papers, the thesis statement must take a clear and definitive stand on a particular issue or debate. It should present a strong, persuasive argument that is supported by evidence throughout the paper.

In argumentative papers, the thesis statement is often controversial or provocative, aiming to convince the reader of a particular viewpoint.

Comparative Papers

The thesis statement in comparative papers should focus on highlighting the similarities and differences between two or more subjects, ideas, or phenomena.

It requires a balanced and nuanced approach, analyzing each subject in relation to the others, and drawing insightful comparisons and contrasts.

Narrative Papers

Though less common in academic writing, narrative essays still require a thesis statement. In these papers, the thesis sets the tone for a story or personal experience, focusing on a central theme, message, or lesson.

The thesis statement in narrative essays often reflects the writer’s personal insights or learnings from the experience being shared.

Can AI write my thesis?

Can i use chatgpt for my thesis, can universities detect ai writing, which ai is best for writing a thesis, conclusion: embracing ai for effective thesis writing.

AI writing tools have revolutionized my approach to thesis writing. They serve as powerful aids, but the core of thesis writing still relies on individual critical thinking and expertise.

Now that you know how to craft effective thesis statements with AI writing tools, why not check out what else AI writing tools can help you do? Check out the articles in the how-to guide to learn more and you can enhance your understanding of our AI terms by visiting our AI glossary as well.

Digital marketing enthusiast by day, nature wanderer by dusk. Dave Andre blends two decades of AI and SaaS expertise into impactful strategies for SMEs. His weekends? Lost in books on tech trends and rejuvenating on scenic trails.

How to Write Engaging Film Reviews with AI Writing Tools?

How to Write Engaging Content on Pets and Animals with AI Writing Tools?

How To Write Effective Opinion Pieces with AI Writing Tools?

How to Write Effective Policy Papers with AI Writing Tools?

Artificial Intelligence

Background Information
Getting started
Browse Journals
Dissertations & Theses
Datasets and Repositories
Research Data Management 101
Scientific Writing
Find Videos
Related Topics
Quick Links
Ask Us/Contact Us

FIU dissertations

Non-FIU dissertations

Many universities provide full-text access to their dissertations via a digital repository. If you know the title of a particular dissertation or thesis, try doing a Google search.

Aims to be the best possible resource for finding open access graduate theses and dissertations published around the world with metadata from over 800 colleges, universities, and research institutions. Currently, indexes over 1 million theses and dissertations.

This is a discovery service for open access research theses awarded by European universities.

A union catalog of Canadian theses and dissertations, in both electronic and analog formats, is available through the search interface on this portal.

There are currently more than 90 countries and over 1200 institutions represented. CRL has catalog records for over 800,000 foreign doctoral dissertations.

An international collaborative resource, the NDLTD Union Catalog contains more than one million records of electronic theses and dissertations. Use BASE, the VTLS Visualizer or any of the geographically specific search engines noted lower on their webpage.

Indexes doctoral dissertations and masters' theses in all areas of academic research includes international coverage.

ProQuest Dissertations & Theses global

Related Sites

<< Previous: Browse Journals
Next: Datasets and Repositories >>
Last Updated: Apr 4, 2024 8:33 AM
URL: https://library.fiu.edu/artificial-intelligence

Information

Fiu libraries floorplans, green library, modesto a. maidique campus, hubert library, biscayne bay campus.

Directions: Green Library, MMC

Directions: Hubert Library, BBC

CUNY Academic Works

< Previous

Home > Hunter College > Theses and Dissertations > 1124

Theses and Dissertations

Art in the age of algorithmic automation and artificial intelligence.

Milly Skellington , CUNY Hunter College Follow

Date of Award

Fall 1-5-2024

Document Type

Degree name.

Master of Fine Arts (MFA)

First Advisor

Reiner Leist

Academic Program Adviser

Carrie Moyer and Lisa Corinne Davis

The 21st century is examined in order to understand how the artists tools have gained unprecedented autonomy.

Recommended Citation

Skellington, Milly, "Art In The Age Of Algorithmic Automation and Artificial Intelligence" (2024). CUNY Academic Works. https://academicworks.cuny.edu/hc_sas_etds/1124

Included in

Art Practice Commons

To view the content in your browser, please download Adobe Reader or, alternately, you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.

Colleges, Schools, Centers
Disciplines

Advanced Search

Notify me via email or RSS

Author Corner

Submit Work
Hunter College

Home | About | FAQ | My Account | Accessibility Statement

Privacy Copyright

UC San Diego Joins NIH 'Bridge to Artificial Intelligence Program'

Researchers at University of California San Diego School of Medicine have been selected to lead components of the National Institutes of Health (NIH) Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) program.

Two faculty in the Bioinformatics and Systems Biology Graduate Program have leading roles in this project. Trey Ideker, PhD, professor at UC San Diego School of Medicine, will serve as principal investigator for one of the Data Generation Projects. Lucila Ohno-Machado, MD, PhD, professor and associate dean for informatics and technology at UC San Diego School of Medicine, will serve as a principal investigator for the Bridge Center.

“This is the first time NIH has invested in biomedical AI at this scale, and we are thrilled to be a part of it,” said Ideker. “UC San Diego has proven itself to be a pioneer in clinical and research AI technology, but this funding will help cement our place in the AI revolution.”

Cookie Acknowledgement

This website uses cookies to collect information to improve your browsing experience. Please review our Privacy Statement for more information.

College of Engineering
News Center

Auburn Engineering to offer new artificial intelligence programs beginning this fall

Published: Apr 10, 2024 10:05 AM

By Joe McAdory

Auburn Engineering’s Department of Computer Science and Software Engineering (CSSE) will offer three artificial intelligence (AI) engineering degree and certificate programs beginning in Fall 2024.

The programs — master’s degree in AI engineering, graduate certificate in AI engineering and undergraduate certificate in AI engineering — will provide various levels of technical depth to broaden students’ skillsets as they enter the workforce, CSSE Chair Hari Narayanan, said.

“Having a solid artificial intelligence education is a growing demand for the engineering workforce and we look forward to supplying organizations with highly skilled graduates,” Narayanan said. “Employers are looking for people with AI-related skills… even in the most unlikely places. Artificial intelligence promises to revolutionize sectors such as business, defense, education, government and health care. To succeed in these roles, people need to understand how to utilize AI systems productively and efficiently. We aim to meet that need.”

The 30-hour master’s degree curriculum in AI engineering, which can be taken online or on-campus, is open to all students who have already earned a baccalaureate degree from an institution of recognized standing, either in computer science, software engineering, or another STEM discipline, and to those who have professional experience related to computing or IT and familiarity with mathematics. This program requires students to take three core courses — artificial intelligence, machine learning and data mining — and seven technical electives related to AI.

“The graduate program provides in-depth training, including the possibility for students to specialize,” Narayanan said. “For example, a student could say, ‘I want in-depth training in AI, but I also want to know how to use it within cyber security, computer networks, or something else.’”

In the graduate program, students will learn how to:

Develop algorithms and methodologies for AI and machine learning systems and technologies.
Incorporate software engineering principles to analyze, design and implement AI and machine learning software.
Apply AI and machine learning techniques to solve complex engineering problems and problems of societal importance.
Deliver written and oral presentations to non-technical and technical audiences.

Like the standard graduate degree program, admission into the graduate certificate program in AI engineering is open to all students with a baccalaureate degree from an institution of recognized standing, either in computer science, software engineering, another STEM discipline or have relevant professional experience and background knowledge.

“The graduate certificate might be appealing to students who have been working within industry for a while and want to upskill,” Narayanan said. “Maybe not enough to design and build a new AI system, but enough to make decisions about what AI technologies and tools to use and how best to use them.”

This 12-hour program, available on-campus or online, includes courses in AI, machine learning and data mining, along with an approved AI-related elective.

Narayaran said the undergraduate certificate in AI engineering is currently open only to CSSE majors.

“Students in this program will develop the deep technical knowledge and skills to solve real-world problems and issues faced by government, industry and society,” he said. “Blending this AI certificate with their chosen CSSE major in computer science or software engineering can be very advantageous for these students.”

The undergraduate certificate program’s 12-hour curriculum models the graduate certificate and includes courses in AI, machine learning and data mining with an approved AI-related elective.

“Developing cutting-edge programs like these — and meeting the growing needs of industry — is expected of leading engineering colleges in the nation,” Narayanan said. “In fact, these programs on engineering AI systems are the first of their kind in Alabama.”

Among the largest departments at Auburn University, CSSE currently offers a variety of degree and certificate programs, including B.S. in Computer Science, Bachelor of Software Engineering, Bachelor of Computer Science (online), Computer Science Minor, Undergraduate Cyber Defense Certificate, M.S. in Computer Science and Software Engineering (thesis or non-thesis options), M.S. in Cybersecurity Engineering, Graduate Certificate in Cybersecurity Engineering, M.S. in Data Science and Engineering (data science or data engineering options), Graduate Certificate in Data Engineering, and Doctor of Philosophy.

Visit here for more information about computer science and software engineering at Auburn University.

New programs include a master’s degree in AI engineering, graduate certificate in AI engineering and undergraduate certificate in AI engineering.

Featured Faculty

Computer Science and Software Engineering

Recent Headlines

Mobile Site
Staff Directory
Advertise with Ars

Filter by topic

Biz & IT
Gaming & Culture

Front page layout

Mourning a philosophical giant —

Philosopher daniel dennett dead at 82, part of the "new atheist" movement, best known for work on consciousness, free will..

Jennifer Ouellette - Apr 19, 2024 11:45 pm UTC

Daniel Dennett seated against black background in blue shirt, bowtie and dark jacket

World renowned philosopher Daniel Dennett, who championed controversial takes on consciousness and free will among other mind-bending subjects, died today at the age of 82.

( Full disclosure: This loss is personal. Dennett was a friend and colleague of my spouse, Sean Carroll . Sean and I have many fond memories of shared meals and stimulating conversations on an enormous range of topics with Dan over the years. He was a true original and will be greatly missed .)

Stunned reactions to Dennett's unexpected passing began proliferating on social media shortly after the news broke. "Wrenching news. He's been a great friend and incredible inspiration for me throughout my career," the Santa Fe Institute's Melanie Mitchell, author of Artificial Intelligence: A Guide for Thinking Humans , wrote on X . "I will miss him enormously."

"He was a towering figure in philosophy and in particular in the philosophy of AI," roboticist Rodney Brooks (MIT, emeritus) wrote on X , bemoaning that he'd never replied to Dennett's last email from 30 days ago. "Now we have only memories of him.

A 2017 New Yorker profile described Dennett as "a cross between Darwin and Santa Claus," with "a fluffy white beard and a round belly." That jolly appearance was accompanied by an intellectual ferocity—generously embellished with his sparkling wit—as he battled such luminaries as Stephen J. Gould, John Searle, Noam Chomsky, David Chalmers, Roger Penrose, and Richard Lewontin, among others, over consciousness and evolution, free will, AI, religion, and many other topics.

Dennett's many books, while dense, nonetheless sold very well and were hugely influential, and he was a distinguished speaker in great demand. His 2003 TED talk, " The Illusion of Consciousness ," garnered more than 4 million views. While he gained particular prominence as a leader of the " New Atheist " movement of the early 2000s—colorfully dubbed one of the " Four Horsemen of New Atheism " alongside Richard Dawkins, Christopher Hitchens, and Sam Harris—that was never his primary focus, merely a natural extension of his more central philosopical concerns.

David Wallace, Sean Carroll, and Daniel Dennett at the Santa Fe Institute in March.

David Wallace, historian and philosopher of science at the University of Pittsburgh, offered Ars Technica this succinct summation of Dennett's extraordinary influence:

To me, Dan Dennett exemplified what it means to do philosophy in an age of science. He once said that there was no such thing as philosophy-free science, only science that didn’t interrogate its philosophical assumptions; equally, he saw more deeply than almost anyone that the deepest traditional questions of philosophy, from free will to consciousness to metaphysics, were irreversibly transformed by modern science, most especially by natural selection. His approach, as much as his own towering contributions, has inspired generations of philosophers, far beyond cognitive science and the philosophy of mind (his ideas have been influential in the interpretation of quantum theory, for instance). He was one of the great philosophers of the last century, and one of the very few whose work has been transformative outside academic philosophy.

"Dan Dennett was the embodiment of a natural philosopher—someone who was brilliant at the careful conceptual analysis that characterizes the best philosophy, while caring deeply about what science has to teach us about the natural world," Johns Hopkins University physicist and philosopher Sean Carroll told Ars. "At the same time, he was the model of a publicly-engaged academic, someone who wrote substantive books that anyone could read and who had a real impact on the wider world. People like that are incredibly rare and precious, and his passing is a real loss."

Born in Boston in 1942, Dennett's father was a professor of Islamic history who became a secret agent for the OSS during World War II, posing as a cultural attaché at the American Embassy in Beirut. Dennett spent his early childhood there until his father was killed in a plane crash while on a mission to Ethiopia. Dennett, his mother, and two sisters returned to Boston after that, and his family assumed he would attend Harvard just like his late father. But after graduating from the Phillips Exeter Academy, Dennett opted to attend Wesleyan University instead—at least until be came across Harvard logician and philosopher W.V.O. Quine 's 1963 treatise, From a Logical Point of View .

Dennett ended up transferring to Harvard to study under Quine and become a philosopher, initially intent on proving Quine wrong. By the time he was a graduate student at Oxford University, he was known among his fellow students as "the village Quinean." In his 2023 memoir, I've Been Thinking , Dennett traced his interest in applying his field to questions of science began during this period. He recalled experiencing the universal sensation of one's hand falling asleep and feeling like an alien thing, rather than part of one's own body. He wondered what was going on in the body and the brain.

Dennett at a group dinner in February 2023. He was the inaugural speaker for the Johns Hopkins Natural Philosophy Forum Distinguished Lecture series.

"The other philosophers thought, that’s not philosophy. I said, well, it should be," he told Tufts Now last year. "So I started learning. I didn’t even know what a neuron was back then in the early ’60s, but I soon learned. I was lucky to get in on the ground floor of cognitive neuroscience. Some of the early pioneers in that field were my heroes and mentors and friends."

Dennett's first academic position was at the University of California, Irvine, and a revised version of his doctoral thesis became his first book: 1969's Content and Consciousness . He moved to Tufts University in 1971, where he remained for the rest of his career. One of Dennett's earliest collaborators was Douglas Hofstadter, author of the bestselling Gödel, Escher, Bach: an Eternal Golden Braid , who called Dennett "a lodestar in my life" in an email [quoted with permission] to colleagues after hearing of the latter's death:

Dan was a deep thinker about what it is to be human. Quite early on, he arrived at what many would see as shocking conclusions about consciousness (essentially that it is just an emergent effect of physical interactions of tiny inanimate components), and from then on, he was a dead-set opponent of dualism (the idea that there is an ethereal nonphysical elixir called “consciousness”, over and above the physical events taking place in the enormously complex substrate of a human or animal brain, and perhaps that of a silicon network as well). Dan thus totally rejected the notion of “qualia” (pure sensations of such things as colors, tastes, and so forth), and his arguments against the mystique of qualia were subtle but very cogent.

Dennett was a a confirmed compatibilist on the fiercely debated subject of free will, meaning that he saw no conflict between philosophical determinism and free will. "Our only notable divergence was on the question of free will, which Dan maintained exists, in some sense of 'free,' whereas I just agreed that 'will' exists, but maintained that there is no freedom in it," Hoftstadter recalled.

Johns Hopkins philosopher Jenann Ismael recalled corresponding with Dennett after her own book on free will, How Physics Makes Us Free, was published in 2016. She had not yet met Dennett, but his work was naturally a significant influence, even though her book was largely critical of his stance on the subject. Ismael opened her book by discussing Dennett's fictional short story, " Where Am I? ", calling it "the best of piece of philosophical fiction ever written." (Check out this short film based on the story, starring Dennett himself uttering such immortal lines as, "They made a sparkling new vat for my brain.")

Dennett read her book and emailed Ismael with a few notes—not about how he felt she'd misrepresented his views (which he deemed of "no matter") but correcting her mistakes about the plot of his short story. "It turns out I got the story wrong," Ismael told Ars. "I'd read it so long ago, I just embellished it in my head and embarrassingly never realized. Where I criticized him in my book, he wasn't as keen to correct me as he was excited to talk about the ideas."

She found him to be filled with infectious warmth. "It was true that he could suck the air out of a room when he entered and even sitting at a round dinner table, he somehow became the center of it, he took possession of the discussion," said Ismael. "But he also paid close attention to people, read voraciously, listened to and heard what others were saying, taking what he could and disseminating what he learned. He had immense curiosity and he wanted to share everything that he learned or liked."

reader comments

Channel ars technica.

Election 2024
Entertainment
Newsletters
Photography
Personal Finance
AP Investigations
AP Buyline Personal Finance
AP Buyline Shopping
Press Releases
Israel-Hamas War
Russia-Ukraine War
Global elections
Asia Pacific
Latin America
Middle East
Election Results
Delegate Tracker
AP & Elections
Auto Racing
2024 Paris Olympic Games
Movie reviews
Book reviews
Personal finance
Financial Markets
Business Highlights
Financial wellness
Artificial Intelligence
Social Media

Italian court drops charges against migrant rescue ship crews and ends long-running legal battle

Copy Link copied

ROME (AP) — An Italian court on Friday dismissed a long-running case against rescue ship crews of three humanitarian organizations, dropping charges accusing them of collaborating with smugglers as they helped rescue thousands of migrants at sea.

The judges in the Sicilian city of Trapani decided not to proceed to trial against 10 crew members involved in the so-called Iuventa case, named after the rescue vessel operated by German nonprofit Jugend Rettet.

Staff members from Jugend Rettet, Save The Children and Doctors Without Borders were fully acquitted of all charges of aiding and abetting illegal immigration.

Italian prosecutors started the case in 2017, accusing the crew members of serving as “taxis” for migrants, allegedly coordinating their search-and-rescue actions with human traffickers off the coast of Libya. They were also accused of returning dinghies and boats to smugglers to be reused, while rescuing migrants in the Mediterranean Sea whose lives were not in real danger.

Italy’s Interior Ministry had joined the lawsuit as plaintiff.

The court on Friday followed the surprise recommendation by prosecutors in February to dismiss all charges in the case, which the organizations had slammed for criminalizing their activity in the Mediterranean.

In this image provided by Pittsburgh Public Safety, Pittsburgh area water rescue team rescued a woman trapped in a car sinking in rising waters along a section of Route 51 east of the city Thursday night, April 11, 2024, in Pittsburg. Flash flooding caused by relentless heavy rains that soaked western Pennsylvania spurred numerous rescues and evacuations in the region, but no injuries were reported. (Pittsburgh Public Safety via AP)

More than 20 people had been involved in the inquiry over the years, including boat captains, heads of mission and legal representatives, facing charges carrying sentences of up to 20 years.

“The truth has been recognized,” Save the Children said after the ruling.

The Iuventa crew members said “the case marked the onset of a public smear campaign against civil sea rescue, aimed at legitimizing crackdowns on rescue efforts.”

Jugend Rettet said its rescue ship had aided over 14,000 people in distress from 2016 until its seizure in the summer of 2017, when the case started.

Doctors Without Borders, also known by its French acronym MSF, stressed in a statement it had faced “seven years of false accusations, defamatory statements, and a blatant criminalization campaign towards organizations performing search and rescue operations at sea.”

Italian authorities began to focus on the issue in 2016, as Rome’s then center-left government was struggling to manage a double-digit increase in the number of migrants reaching the country’s coasts in a desperate attempt to reach Europe.

The current right-wing government led by Premier Giorgia Meloni has further stiffened Rome’s tough stance against migrant rescues in the Mediterranean, limiting ships to one sea rescue at a time, and forcing them to dock at an assigned port — rules the charities say are severely hitting rescues.

Taylor Swift’s new album leaked, but it was only half the story

The frenzy surrounding Taylor Swift’s new album “The Tortured Poets Department” kicked off a day early, as the singer became one of the most recent victims of a song leak.

What was purported to be the entire album spread online Thursday, origin unknown. Some fans spent the day refusing to listen to the 17 illicit tracks. Others were fooled by artificial intelligence-generated fakes, or frustrated by social media sites that seemed to delete leaked tracks as soon as they were posted.

Countless others found and devoured the runaway songs. Premature reviews and amateur analyses of the album flooded fan sites despite pleas from devoted Swifties to wait for the official release.

Maybe they should have. Swift dropped “Tortured Poets” as planned at midnight, which confirmed that the leaked tracks were indeed real. But two hours later, she made the surprise announcement that it was actually a double album and released another 15 tracks. The leak was barely half the album.

Was this a win or loss for Swift? It’s hard to say. It was at least the fourth leak of her music in a decade, despite her efforts to quash the phenomenon. But a leak today might mean something very different than it did before the advent of the streaming age.

“We’re living in the attention economy now,” said Ted Cohen, an adviser for streaming platforms. “At the end of the day, it increases awareness.”

Cohen recalled a famous industry quote: “If your artist isn’t being pirated, your artist isn’t popular.” Some managers in the early 2000s “literally would use tracking of music piracy to correlate fan interest in an artist,” he recalled.

Labels sometimes intentionally leaked music to invite public controversy, he added. But leaks don’t carry quite the same allure anymore now that listeners overwhelmingly stream music rather than buy albums .

Digital leaks have been around since the ‘90s, when software evolved to a point where songs could be easily uploaded. In 1993, Depeche Mode’s new CD “Songs of Faith and Devotion” leaked in online chatrooms, via audio files that took about 30 minutes each to download through a dial-up connection, Pitchfork reported. In 2002, Eminem’s critically acclaimed “The Eminem Show” leaked weeks early and ended up on bootleg CDs.

“People want access to that for the novelty of the new stuff. And if they can get it, and they can get it for free because it’s been leaked, that’s something attractive to a rabid fan,” said Ali Aydar, who worked as a senior director of technology at Napster from 1999 to 2002 — the year the popular file-sharing company filed for bankruptcy amid a flood of copyright infringement lawsuits. Aydar said the first lawsuit was filed by Metallica, after the band discovered one of their demos had hit radio stations after being leaked to Napster.

The Style section

But leaks had the potential to be much more disruptive then, Aydar said. During the Napster era, an uploaded track could spread across the internet to tens of millions of people within hours. Now, he said, platforms such as Spotify and YouTube have safeguards to prevent leaks from spreading — as was seen on Thursday with the “Tortured Poets” leak.

Sony reportedly sued a Swedish man for nearly a quarter-million dollars in 2013, accusing him of leaking tracks from Beyoncé’s 2011 album, “4.” In 2020, Dua Lipa moved the release date of her album “Future Nostalgia” up by one week after some of her songs leaked early online. “Songs that were never meant to come out get leaked,” she said in an interview years later. “You’re figuring it out and you’re going through all the really bad ideas and then all of a sudden people hear that and you’re like, ‘that’s not me! It’s me figuring out me.’”

“Friends don’t let friends listen to Harry’s House before May 20,” Sony tweeted in 2022, after the new Harry Styles album “Harry’s House” surfaced online about a month before its official release date.

City Girls rapper Yung Miami told The Washington Post she was at the wake for her son’s father when she heard the news that her album “City on Lock,” was leaked in 2020.

“You just have it preplanned in your head. You work with your team. It’s a whole creative thought process, so everything is literally flushed down the toilet,” she said. “To go and leak someone’s hard work … is always devastating.”

Swift has also experienced her fair share of song leaks over the years. She said in a 2010 Rolling Stone interview that she cried after learning “Mine,” a track from her third studio album “Speak Now,” was leaked 12 days early. Her team decided to rush the song to iTunes.

“Every day that the album doesn’t leak is a victory,” the president of Big Machine Label Group told Billboard in 2012 , as the label prepared to released Swift’s fourth album, “Red.” It leaked days ahead of its release date.

So did her fifth album, “1989,” in 2014. And albums six (“Reputation”) and seven (“Lover”) spilled, too, mere hours before their official drops.

Swift has clamped down on plays of her prereleased music over the years. Dancers in her “End Game” music video had to perform choreography to “click tracks” in lieu of the actual music, she revealed in a behind-the-scenes video .

“I remember when I did a song with her for her album, I was in San Francisco and they sent someone with a locked briefcase with an iPad and one song on it,” Ed Sheeran told Brazilian media outlet Capricho in 2017. “They asked if I like it and I was like ‘Yeah’ and then they took it back.”

Swift has important allies in her war against leaks: a significant portion of her fan base not only refuses to listen to them, but they also police the internet and implore others to do the same.

“It was the first time I’ve ever had an album leak without it trending on Twitter — because my fans protected it.” Swift told NPR after the “1989” leak in 2014. “Anytime they’d see an illegal post of it, they’d comment, ‘Why are you doing this? Why don’t you respect the value of art? Don’t do this.’”

“Taylor’s fans are very protective of her vision,” said Georgia Carroll, a fan studies researcher who wrote her PhD thesis on Swift’s fandom. “The narrative becomes ‘real fans wouldn’t listen to leaks’ and ‘real fans will support Taylor and wait until it’s actually out,’ with critiques and attacks on those who do admit to listening.”

Some Swift-related Reddit pages limited discussions of leaked tracks and moderators threatened to ban users who attempt to share the files. On X, the search phrase “taylor swift leak” turned up without results — most likely because the site blocked the phrase.

The “Tortured Poets” leak was also novel for colliding with the new phenomenon of AI-generated audio. As news of the leak spread Thursday, many would-be pirates were tricked into downloading what sounded like machine-generated tracks posing as Swift songs.

That might have cut back on the number of downloads for the actual leak, but it also created some comparisons between the AI tracks and the real music.

“The a.i version of fortnite with post malone was so gooooooood,” one poster on Reddit said after listening to a fake version of the first track on “Tortured Poets.” “Im sad when the real version is opposite of that."

Taylor Swift’s new album leaked, but it was only half the story 52 minutes ago Taylor Swift’s new album leaked, but it was only half the story 52 minutes ago
Australian conductor Simone Young shoots for the stars with the NSO 1 hour ago Australian conductor Simone Young shoots for the stars with the NSO 1 hour ago
A guide to the Easter eggs and name-drops on Taylor Swift’s new album 1 hour ago A guide to the Easter eggs and name-drops on Taylor Swift’s new album 1 hour ago

IMAGES

Artificial Intelligence, Are the Machines Taking over Free Essay Example
artificial intelligence thesis mit
10 Mind-Blowing Thesis Statements for Artificial Intelligence in 2024
How to Write a Better Thesis Statement Using AI (2023 Updated)
Artificial Intelligence Essay
Essay on Artificial Intelligence

VIDEO

What is a Thesis Statement?
RM Gerry Connolly's Opening Statement: Artificial Intelligence
How do I write my PhD thesis about Artificial Intelligence, Machine Learning and Robust Clustering?
How to Write Research Paper / Thesis Using Chat GPT 4 / AI (Artificial Intelligence)
d!talk Keynote Speech / Statement Prof. Dr. Christian Bauckhage, IAIS
ScholarWriterAI

COMMENTS

The Main Topics for Coursework or a Thesis Statement in Artificial
Deep learning (DL) as a Thesis Topic. Deep Learning is a subset of ML where learning imitates the inner workings of the human brain. It uses artificial neural networks to process data and make decisions. The web-like networks take a non-linear approach to processing data which is superior to traditional algorithms that take a linear approach.
Artificial Intelligence & Machine Learning Thesis Statement Examples
Bad Thesis Statement Examples. Overly Broad: "Artificial intelligence is changing the world.". While true, this statement is overly broad, providing no clear direction or focus for research. Lack of Clear Argument: "AI and ML are important in data analysis.". This statement, while factual, lacks a clear argument or focus, not providing ...
12 Best Artificial Intelligence Topics for Thesis and Research
In this blog, we embark on a journey to delve into 12 Artificial Intelligence Topics that stand as promising avenues for thorough research and exploration. Table of Contents. 1) Top Artificial Intelligence Topics for Research. a) Natural Language Processing. b) Computer vision. c) Reinforcement Learning. d) Explainable AI (XAI)
8 Best Topics for Research and Thesis in Artificial Intelligence
So without further ado, let's see the different Topics for Research and Thesis in Artificial Intelligence!. 1. Machine Learning. Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!!!)
How to Write a Better Thesis Statement Using AI (2023 Updated)
Once you have a clear idea of the topic and what interests you, go on to the next step. 2. Ask a research question. You know what you're going to write about, at least broadly. Now you just have to narrow in on an angle or focus appropriate to the length of your assignment.
10 Thesis Statement AI Examples to Ace Your Research in 2024
These thesis statements cover a wide range of topics and are designed to be both informative and engaging. So, let's dive in and explore these examples! 1. The Impact of Artificial Intelligence on Job Market. Artificial Intelligence (AI) has revolutionized various industries, including the job market. This thesis statement explores the impact ...
Artificial Intelligence Thesis Statement
An artificial intelligence thesis statement is a statement of intent that outlines the purpose and direction of research into artificial intelligence. It provides a framework for the research and is used to guide the project throughout its development. In this statement, the researcher outlines the questions they will be addressing and the ...
PDF The impact of artificial intelligence amongst higher ...
The impact of artificial intelligence amongst higher education students Number of pages and appendix pages 35 + 2 This thesis is about how artificial intelligence is impacting students in universities and universi-ties of applied sciences. Artificial intelligence has developed a lot in the past years, each day
PDF The use of artificial intelligence (AI) in thesis writing
Text generator (chatbot) based on artificial intelligence and developed by the company OpenAI. Aims to generate conversations that are as human-like as possible. Transforms input into output by "language modeling" technique. Output texts are generated as the result of a probability calculation.
Artificial Intelligence and its Implications on the Future of Humanity
Jack MacPhee. Fleming. ST112WA. May 16, 2018. Artificial Intelligence and its Implications on the Future of Humanity. Though some may think of robots taking over the world when they envision AI, artificial intelligence is a very broad term that includes things from Apple's Siri and Amazon's Alexa, to, more recently, more complex things like ...
PDF The implementation of artificial intelligence and its future ...
to judge intelligence based on communication capabilities. In 1956, the work of Allen Newell, J. C. Shaw and Herb Simon was presented at the landmark conference on artificial intelligence which took place in Dartmouth. That conference might as well have engraved the initials "AI" into marble as artificial intelligence got its name then and ...
PDF Master in Artificial Intelligence Master Thesis
Master in Artificial Intelligence Master Thesis Analysis of Explainable Artificial Intelligence on Time Series Data Author: Supervisors: NataliaJakubiak MiquelSànchez-Marrè CristianBarrué Department: DepartmentofComputerScience Facultat d'Informatica de Barcelona (FIB) Universitat Politècnica de Catalunya (UPC) - BarcelonaTech October 2022
Artificial Intelligence · University of Basel · Completed Theses
This thesis contributes two approaches to create witnesses for unsolvable planning tasks. Inductive certificates are based on the idea of invariants. ... Classical Planning is a branch of artificial intelligence that studies single agent, static, deterministic, fully observable, discrete search problems. A common challenge in this field is the ...
PDF The Utilization of Artificial Intelligence in Healthcare and Its
This thesis is dedicated to my family who have supported me throughout my education. I thank them for their endless love, support and encouragement of lifelong ... "artificial intelligence" in 1956 at the Dartmouth Summer Research Project in Artificial Intelligence conference. Other "founding fathers" of artificial intelligence include Alan
PDF ARTIFICIAL INTELLIGENCE AND ITS IMPACT ON WORKFORCE
In the book Introducing Artificial Intelligence: A Graphic Guide of Henry Brighton, he divided AI into 2 forms: Strong AI and Weak AI (Brighton 2015). There is nothing much to talk about Strong AI, so called Artificial General Intelligence (AGI). AGI is a form of intelligent machine which can perform completely all kind of task as a normal human.
5 (thesis) Statements about Artificial Intelligence : r/artificial
5 (thesis) Statements about Artificial Intelligence. Discussion. AI is a branch of philosophy and not of computer science. AI is not a revolutionary but a transforming technology. We have only seen & experienced the tip of the iceberg in terms of AI. AI is an accelerator of evolution.
PDF The Effects of Artificial Intelligence in the Future Economy
2 Artificial Intelligence Artificial intelligence, also known as an expert system is the theory of machines imitating the ability of cognitive thinking (Kaplan & Haenlein 2019). AI is the science of building intelligent machines from large volumes of data and learning from experience to perform human-like tasks.
Artificial Intelligence Thesis [List of Top 10 Tools]
Artificial intelligence is the technology where humans' intelligence is replicated by the supercomputers in the network.Artificial intelligence is often called AI. It is the main branch of computer science to stimulate smart devices with human analytical behaviours. "This article is completely contented with the interesting concepts related to doing the artificial intelligence thesis"
Artificial Intelligence in Healthcare
Although machine learning and AI collaboration will transform a wide range of areas such as cloud database, decision-making process, medical processes, mental healthcare radiology, and telehealth, there are challenges associated with its adoption. We will write a custom essay on your topic. 809 writers online.
The impact of artificial intelligence on human society and bioethics
Artificial intelligence-based surgical contribution. AI-based surgical procedures have been available for people to choose. Although this AI still needs to be operated by the health professionals, it can complete the work with less damage to the body. The da Vinci surgical system, a robotic technology allowing surgeons to perform minimally ...
AI for thesis writing
Artificial Intelligence offers a supportive hand in thesis writing, adeptly navigating vast datasets, suggesting enhancements in writing, and refining the narrative. With the integration of AI writing assistant, instead of requiring you to manually sift through endless articles, AI tools can spotlight the most pertinent pieces in mere moments.
How to Craft Effective Thesis Statements With AI Writing Tools?
Step 1: Identifying the Topic. The first step in thesis writing is identifying a relevant and engaging topic. AI tools such as OpenAI's GPT-4 are invaluable in this stage, offering up-to-date suggestions on trending and significant topics. These tools analyze current research and discussions in various fields, providing me with a broad range ...
FIU Libraries: Artificial Intelligence: Dissertations & Theses
Many universities provide full-text access to their dissertations via a digital repository. If you know the title of a particular dissertation or thesis, try doing a Google search. OATD (Open Access Theses and Dissertations) Aims to be the best possible resource for finding open access graduate theses and dissertations published around the world with metadata from over 800 colleges ...
"Art In The Age Of Algorithmic Automation and Artificial Intelligence
Art In The Age Of Algorithmic Automation and Artificial Intelligence. Author. Milly Skellington, CUNY Hunter College Follow. Date of Award. Fall 1-5-2024. Document Type. Thesis. Degree Name. Master of Fine Arts (MFA) Department. Art. First Advisor. Reiner Leist. Academic Program Adviser. Carrie Moyer and Lisa Corinne Davis. Abstract ...
UC San Diego Joins NIH 'Bridge to Artificial Intelligence Program'
Researchers at University of California San Diego School of Medicine have been selected to lead components of the National Institutes of Health (NIH) Common Fund's Bridge to Artificial Intelligence (Bridge2AI) program. Two faculty in the Bioinformatics and Systems Biology Graduate Program have leading roles in this project.
Auburn Engineering to offer new artificial intelligence programs
Auburn Engineering's Department of Computer Science and Software Engineering (CSSE) will offer three artificial intelligence (AI) engineering degree and certificate programs beginning in Fall 2024.. The programs — master's degree in AI engineering, graduate certificate in AI engineering and undergraduate certificate in AI engineering — will provide various levels of technical depth to ...
Generation Z is unprecedentedly rich
Artificial intelligence could destabilise the global economy, even if youngsters may in time be better placed to benefit from the disruption. For now, though, Generation Z has a lot to be happy about.
Philosopher Daniel Dennett dead at 82
Enlarge / Daniel Dennett, a leading philosopher with provocative takes on consciousness, free will, and AI, has died at 82. Alonso Nichols/Tufts University. 0. World renowned philosopher Daniel ...
Italian court drops charges against migrant rescue ship crews and ends
Artificial Intelligence Social Media Lifestyle. Religion. AP Buyline Personal Finance. AP Buyline Shopping. ... Brazil Supreme Court strikes down military intervention thesis in symbolic vote for democracy. ... stressed in a statement it had faced "seven years of false accusations, defamatory statements, and a blatant criminalization campaign ...
Why Taylor Swift's 'Tortured Poets Department' leak is a big deal
Taylor Swift's new, double album, "The Tortured Poets Department: The Anthology," dropped on April 19. It includes 31 new songs about love, loss, griefand more. (Video: Allie Caren, Sarah ...

The Main Topics for Coursework or a Thesis Statement in Artificial Intelligence

About The Author

Paul Calderon

Leave a Reply Cancel Reply

Most Popular

How to Cite Personal Communication in APA

Study Reveals AI Text Detectors’ Accuracy Can Drop to 17%. What Is The Reason?

Good Thesis Statement Examples

Bad Thesis Statement Examples

Comments (0)

Cancel reply

More from Thesis Statement Examples and Samples

Gender & Sexuality Studies Thesis Statement Examples

Criminal Justice Reform Thesis Statement Examples

Sustainable Development Goals (SDGs) Thesis Statement Examples

Forgotten Password?

12 Best Artificial Intelligence Topics for Research in 2024

Training Outcomes Within Your Budget!

Share this Resource

Top Artificial Intelligence Topics for Research

Natural Language Processing

Computer Vision

Reinforcement Learning

Explainable AI (XAI)

Generative Adversarial Networks (GANs)

Robotics and AI

AI in healthcare

AI for social good

Autonomous vehicles

AI ethics and bias

Future of AI

AI and education

Conclusion

Frequently Asked Questions

Get A Quote

Share this course

Your privacy & cookies!

8 Best Topics for Research and Thesis in Artificial Intelligence

1. Machine Learning

2. Deep Learning

3. Reinforcement Learning

4. Robotics

5. Natural Language Processing

6. Computer Vision

7. Recommender Systems

8. Internet of Things

Please Login to comment...

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

How to Write a Better Thesis Statement Using AI (2023 Updated)

Table of contents

Meredith Sell

What Is a Thesis Statement?

Different Kinds of Papers Need Different Kinds of Theses

How to Write (and Develop) a Strong Thesis

1. Identify your essay topic.

2. Ask a research question.

3. Answer the question tentatively.

4. Research the question (and working thesis).

5. Refine your thesis.

6. Get help from AI

Thesis Check: Look for These Three Elements

It’s Essay-Writing Time!

Share This Article:

Finding the OG Writing Assistant – Wordtune vs. QuillBot

What’s a Split Infinitive? Definition + When to Avoid It

Preparing for Graduate School: 8 Tips to Know

Artificial Intelligence Thesis Statement

Challenges of Developing an AI Thesis Statement

Benefits of an AI Thesis Statement

Types of AI Thesis Statements

How to Create an AI Thesis Statement

Researching AI Thesis Statements

Examples of AI Thesis Statements

FAQs About the Artificial Intelligence Thesis Statement

Related Posts

Who Leads In Artificial Intelligence

Why Study Artificial Intelligence

Which Software Is Used To Make Artificial Intelligence

Completed Theses