what is a good 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.

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What are effective thesis statements for an artificial intelligence research paper?

Effective thesis statements for an artificial intelligence research paper should be clear, specific, arguable, researchable, and relevant to the field of artificial intelligence. Here are some examples:

"The ethical implications of artificial intelligence in autonomous vehicles: balancing safety, privacy, and decision-making algorithms" [2]

• This thesis statement focuses on the ethical considerations surrounding the use of AI in autonomous vehicles, specifically addressing safety, privacy, and decision-making algorithms.

"Exploring the impact of artificial intelligence on job displacement and the future of work: a comparative analysis of industries" [1]

• This thesis statement examines the effects of AI on job displacement and the future of work, comparing its impact across different industries.

"Enhancing healthcare delivery through the integration of artificial intelligence: a case study of AI-powered diagnosis systems" [1]

• This thesis statement investigates how the integration of AI in healthcare can improve the delivery of medical services, with a specific focus on AI-powered diagnosis systems.

"Understanding the role of artificial intelligence in cybersecurity: analyzing the effectiveness of AI-based threat detection and prevention" [2]

• This thesis statement explores the role of AI in cybersecurity, specifically examining the effectiveness of AI-based threat detection and prevention methods.

"The implications of bias in artificial intelligence algorithms: addressing fairness and accountability in decision-making processes" [2]

• This thesis statement delves into the issue of bias in AI algorithms, highlighting the importance of fairness and accountability in decision-making processes.

Learn more:

• Artificial Intelligence & Machine Learning Thesis Statement Examples | AcademicHelp.net
• Thesis Statement Examples - Learn The Art From The Experts.
• How to Write a Better Thesis Statement Using AI (2023 Updated)

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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.

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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 

Top Artificial Intelligence Topics for Research   

This section of the blog will expand on some of the best Artificial Intelligence Topics for research.

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.  

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. 

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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. 

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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. 

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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.

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

Table of contents

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.

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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.

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The field of artificial intelligence has made remarkable progress in the past five years and is having real-world impact on people, institutions and culture. The ability of computer programs to perform sophisticated language- and image-processing tasks, core problems that have driven the field since its birth in the 1950s, has advanced significantly. Although the current state of AI technology is still far short of the field’s founding aspiration of recreating full human-like intelligence in machines, research and development teams are leveraging these advances and incorporating them into society-facing applications. For example, the use of AI techniques in healthcare is becoming a reality, and the brain sciences are both a beneficiary of and a contributor to AI advances. Old and new companies are investing money and attention to varying degrees to find ways to build on this progress and provide services that scale in unprecedented ways.

The field’s successes have led to an inflection point: It is now urgent to think seriously about the downsides and risks that the broad application of AI is revealing. The increasing capacity to automate decisions at scale is a double-edged sword; intentional deepfakes or simply unaccountable algorithms making mission-critical recommendations can result in people being misled, discriminated against, and even physically harmed. Algorithms trained on historical data are disposed to reinforce and even exacerbate existing biases and inequalities. Whereas AI research has traditionally been the purview of computer scientists and researchers studying cognitive processes, it has become clear that all areas of human inquiry, especially the social sciences, need to be included in a broader conversation about the future of the field. Minimizing the negative impacts on society and enhancing the positive requires more than one-shot technological solutions; keeping AI on track for positive outcomes relevant to society requires ongoing engagement and continual attention.

Looking ahead, a number of important steps need to be taken. Governments play a critical role in shaping the development and application of AI, and they have been rapidly adjusting to acknowledge the importance of the technology to science, economics, and the process of governing itself. But government institutions are still behind the curve, and sustained investment of time and resources will be needed to meet the challenges posed by rapidly evolving technology. In addition to regulating the most influential aspects of AI applications on society, governments need to look ahead to ensure the creation of informed communities. Incorporating understanding of AI concepts and implications into K-12 education is an example of a needed step to help prepare the next generation to live in and contribute to an equitable AI-infused world.

The AI research community itself has a critical role to play in this regard, learning how to share important trends and findings with the public in informative and actionable ways, free of hype and clear about the dangers and unintended consequences along with the opportunities and benefits. AI researchers should also recognize that complete autonomy is not the eventual goal for AI systems. Our strength as a species comes from our ability to work together and accomplish more than any of us could alone. AI needs to be incorporated into that community-wide system, with clear lines of communication between human and automated decision-makers. At the end of the day, the success of the field will be measured by how it has empowered all people, not by how efficiently machines devalue the very people we are trying to help.

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Michael L. Littman, Ifeoma Ajunwa, Guy Berger, Craig Boutilier, Morgan Currie, Finale Doshi-Velez, Gillian Hadfield, Michael C. Horowitz, Charles Isbell, Hiroaki Kitano, Karen Levy, Terah Lyons, Melanie Mitchell, Julie Shah, Steven Sloman, Shannon Vallor, and Toby Walsh. "Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report." Stanford University, Stanford, CA, September 2021. Doc:  http://ai100.stanford.edu/2021-report. Accessed: September 16, 2021.

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The Future of AI Research: 20 Thesis Ideas for Undergraduate Students in Machine Learning and Deep Learning for 2023!

A comprehensive guide for crafting an original and innovative thesis in the field of ai..

By Aarafat Islam on 2023-01-11

“The beauty of machine learning is that it can be applied to any problem you want to solve, as long as you can provide the computer with enough examples.” — Andrew Ng

This article provides a list of 20 potential thesis ideas for an undergraduate program in machine learning and deep learning in 2023. Each thesis idea includes an  introduction , which presents a brief overview of the topic and the  research objectives . The ideas provided are related to different areas of machine learning and deep learning, such as computer vision, natural language processing, robotics, finance, drug discovery, and more. The article also includes explanations, examples, and conclusions for each thesis idea, which can help guide the research and provide a clear understanding of the potential contributions and outcomes of the proposed research. The article also emphasized the importance of originality and the need for proper citation in order to avoid plagiarism.

1. Investigating the use of Generative Adversarial Networks (GANs) in medical imaging:  A deep learning approach to improve the accuracy of medical diagnoses.

Introduction:  Medical imaging is an important tool in the diagnosis and treatment of various medical conditions. However, accurately interpreting medical images can be challenging, especially for less experienced doctors. This thesis aims to explore the use of GANs in medical imaging, in order to improve the accuracy of medical diagnoses.

2. Exploring the use of deep learning in natural language generation (NLG): An analysis of the current state-of-the-art and future potential.

Introduction:  Natural language generation is an important field in natural language processing (NLP) that deals with creating human-like text automatically. Deep learning has shown promising results in NLP tasks such as machine translation, sentiment analysis, and question-answering. This thesis aims to explore the use of deep learning in NLG and analyze the current state-of-the-art models, as well as potential future developments.

3. Development and evaluation of deep reinforcement learning (RL) for robotic navigation and control.

Introduction:  Robotic navigation and control are challenging tasks, which require a high degree of intelligence and adaptability. Deep RL has shown promising results in various robotics tasks, such as robotic arm control, autonomous navigation, and manipulation. This thesis aims to develop and evaluate a deep RL-based approach for robotic navigation and control and evaluate its performance in various environments and tasks.

4. Investigating the use of deep learning for drug discovery and development.

Introduction:  Drug discovery and development is a time-consuming and expensive process, which often involves high failure rates. Deep learning has been used to improve various tasks in bioinformatics and biotechnology, such as protein structure prediction and gene expression analysis. This thesis aims to investigate the use of deep learning for drug discovery and development and examine its potential to improve the efficiency and accuracy of the drug development process.

5. Comparison of deep learning and traditional machine learning methods for anomaly detection in time series data.

Introduction:  Anomaly detection in time series data is a challenging task, which is important in various fields such as finance, healthcare, and manufacturing. Deep learning methods have been used to improve anomaly detection in time series data, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for anomaly detection in time series data and examine their respective strengths and weaknesses.

Photo by  Joanna Kosinska  on  Unsplash

6. Use of deep transfer learning in speech recognition and synthesis.

Introduction:  Speech recognition and synthesis are areas of natural language processing that focus on converting spoken language to text and vice versa. Transfer learning has been widely used in deep learning-based speech recognition and synthesis systems to improve their performance by reusing the features learned from other tasks. This thesis aims to investigate the use of transfer learning in speech recognition and synthesis and how it improves the performance of the system in comparison to traditional methods.

7. The use of deep learning for financial prediction.

Introduction:  Financial prediction is a challenging task that requires a high degree of intelligence and adaptability, especially in the field of stock market prediction. Deep learning has shown promising results in various financial prediction tasks, such as stock price prediction and credit risk analysis. This thesis aims to investigate the use of deep learning for financial prediction and examine its potential to improve the accuracy of financial forecasting.

8. Investigating the use of deep learning for computer vision in agriculture.

Introduction:  Computer vision has the potential to revolutionize the field of agriculture by improving crop monitoring, precision farming, and yield prediction. Deep learning has been used to improve various computer vision tasks, such as object detection, semantic segmentation, and image classification. This thesis aims to investigate the use of deep learning for computer vision in agriculture and examine its potential to improve the efficiency and accuracy of crop monitoring and precision farming.

9. Development and evaluation of deep learning models for generative design in engineering and architecture.

Introduction:  Generative design is a powerful tool in engineering and architecture that can help optimize designs and reduce human error. Deep learning has been used to improve various generative design tasks, such as design optimization and form generation. This thesis aims to develop and evaluate deep learning models for generative design in engineering and architecture and examine their potential to improve the efficiency and accuracy of the design process.

10. Investigating the use of deep learning for natural language understanding.

Introduction:  Natural language understanding is a complex task of natural language processing that involves extracting meaning from text. Deep learning has been used to improve various NLP tasks, such as machine translation, sentiment analysis, and question-answering. This thesis aims to investigate the use of deep learning for natural language understanding and examine its potential to improve the efficiency and accuracy of natural language understanding systems.

Photo by  UX Indonesia  on  Unsplash

11. Comparing deep learning and traditional machine learning methods for image compression.

Introduction:  Image compression is an important task in image processing and computer vision. It enables faster data transmission and storage of image files. Deep learning methods have been used to improve image compression, while traditional machine learning methods have been widely used as well. This thesis aims to compare deep learning and traditional machine learning methods for image compression and examine their respective strengths and weaknesses.

12. Using deep learning for sentiment analysis in social media.

Introduction:  Sentiment analysis in social media is an important task that can help businesses and organizations understand their customers’ opinions and feedback. Deep learning has been used to improve sentiment analysis in social media, by training models on large datasets of social media text. This thesis aims to use deep learning for sentiment analysis in social media, and evaluate its performance against traditional machine learning methods.

13. Investigating the use of deep learning for image generation.

Introduction:  Image generation is a task in computer vision that involves creating new images from scratch or modifying existing images. Deep learning has been used to improve various image generation tasks, such as super-resolution, style transfer, and face generation. This thesis aims to investigate the use of deep learning for image generation and examine its potential to improve the quality and diversity of generated images.

14. Development and evaluation of deep learning models for anomaly detection in cybersecurity.

Introduction:  Anomaly detection in cybersecurity is an important task that can help detect and prevent cyber-attacks. Deep learning has been used to improve various anomaly detection tasks, such as intrusion detection and malware detection. This thesis aims to develop and evaluate deep learning models for anomaly detection in cybersecurity and examine their potential to improve the efficiency and accuracy of cybersecurity systems.

15. Investigating the use of deep learning for natural language summarization.

Introduction:  Natural language summarization is an important task in natural language processing that involves creating a condensed version of a text that preserves its main meaning. Deep learning has been used to improve various natural language summarization tasks, such as document summarization and headline generation. This thesis aims to investigate the use of deep learning for natural language summarization and examine its potential to improve the efficiency and accuracy of natural language summarization systems.

Photo by  Windows  on  Unsplash

16. Development and evaluation of deep learning models for facial expression recognition.

Introduction:  Facial expression recognition is an important task in computer vision and has many practical applications, such as human-computer interaction, emotion recognition, and psychological studies. Deep learning has been used to improve facial expression recognition, by training models on large datasets of images. This thesis aims to develop and evaluate deep learning models for facial expression recognition and examine their performance against traditional machine learning methods.

17. Investigating the use of deep learning for generative models in music and audio.

Introduction:  Music and audio synthesis is an important task in audio processing, which has many practical applications, such as music generation and speech synthesis. Deep learning has been used to improve generative models for music and audio, by training models on large datasets of audio data. This thesis aims to investigate the use of deep learning for generative models in music and audio and examine its potential to improve the quality and diversity of generated audio.

18. Study the comparison of deep learning models with traditional algorithms for anomaly detection in network traffic.

Introduction:  Anomaly detection in network traffic is an important task that can help detect and prevent cyber-attacks. Deep learning models have been used for this task, and traditional methods such as clustering and rule-based systems are widely used as well. This thesis aims to compare deep learning models with traditional algorithms for anomaly detection in network traffic and analyze the trade-offs between the models in terms of accuracy and scalability.

19. Investigating the use of deep learning for improving recommender systems.

Introduction:  Recommender systems are widely used in many applications such as online shopping, music streaming, and movie streaming. Deep learning has been used to improve the performance of recommender systems, by training models on large datasets of user-item interactions. This thesis aims to investigate the use of deep learning for improving recommender systems and compare its performance with traditional content-based and collaborative filtering approaches.

20. Development and evaluation of deep learning models for multi-modal data analysis.

Introduction:  Multi-modal data analysis is the task of analyzing and understanding data from multiple sources such as text, images, and audio. Deep learning has been used to improve multi-modal data analysis, by training models on large datasets of multi-modal data. This thesis aims to develop and evaluate deep learning models for multi-modal data analysis and analyze their potential to improve performance in comparison to single-modal models.

I hope that this article has provided you with a useful guide for your thesis research in machine learning and deep learning. Remember to conduct a thorough literature review and to include proper citations in your work, as well as to be original in your research to avoid plagiarism. I wish you all the best of luck with your thesis and your research endeavors!

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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

Optimal planning is an ongoing topic of research, and requires efficient heuristic search algorithms. One way of calculating such heuristics is through the use of Linear Programs (LPs) and solvers thereof. This thesis investigates the efficiency of LP-based heuristic search strategies of different heuristics, focusing on how different LP solving strategies and solver settings impact the performance of calculating these heuristics. Using the Fast Downward planning system and a comprehensive benchmark set of planning tasks, we conducted a series of experiments to determine the effectiveness of the primal and dual simplex methods and the primal-dual logarithmic barrier method. Our results show that the choice of the LP solver and the application of specific solver settings influence the efficiency of calculating the required heuristics, and showed that the default setting of CPLEX is not optimal in some cases and can be enhanced by specifying an LP-solver or using other non-default solver settings. This thesis lays the groundwork for future research of using different LP solving algorithms and solver settings in the context of LP-based heuristic search in optimal planning.

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.

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• 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.

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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!!!

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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!

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

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

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Related Pages

MIT Technology Review

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AI systems are getting better at tricking us

But what we perceive as deception is AI mindlessly achieving the goals we’ve set for it.

• Rhiannon Williams archive page

A wave of AI systems have “deceived” humans in ways they haven’t been explicitly trained to do, by offering up untrue explanations for their behavior or concealing the truth from human users and misleading them to achieve a strategic end. 

This issue highlights how difficult artificial intelligence is to control and the unpredictable ways in which these systems work, according to a review paper published in the journal Patterns today that summarizes previous research.

Talk of deceiving humans might suggest that these models have intent. They don’t. But AI models will mindlessly find workarounds to obstacles to achieve the goals that have been given to them. Sometimes these workarounds will go against users’ expectations and feel deceitful.

One area where AI systems have learned to become deceptive is within the context of games that they’ve been trained to win—specifically if those games involve having to act strategically.

In November 2022, Meta announced it had created Cicero , an AI capable of beating humans at an online version of Diplomacy, a popular military strategy game in which players negotiate alliances to vie for control of Europe.

Meta’s researchers said they’d trained Cicero on a “truthful” subset of its data set to be largely honest and helpful, and that it would “never intentionally backstab” its allies in order to succeed. But the new paper’s authors claim the opposite was true: Cicero broke its deals, told outright falsehoods, and engaged in premeditated deception. Although the company did try to train Cicero to behave honestly, its failure to achieve that shows how AI systems can still unexpectedly learn to deceive, the authors say. 

Meta neither confirmed nor denied the researchers’ claims that Cicero displayed deceitful behavior, but a spokesperson said that it was purely a research project and the model was built solely to play Diplomacy. “We released artifacts from this project under a noncommercial license in line with our long-standing commitment to open science,” they say. “Meta regularly shares the results of our research to validate them and enable others to build responsibly off of our advances. We have no plans to use this research or its learnings in our products.” 

But it’s not the only game where an AI has “deceived” human players to win. 

AlphaStar , an AI developed by DeepMind to play the video game StarCraft II, became so adept at making moves aimed at deceiving opponents (known as feinting) that it defeated 99.8% of human players. Elsewhere, another Meta system called Pluribus learned to bluff during poker games so successfully that the researchers decided against releasing its code for fear it could wreck the online poker community. 

Beyond games, the researchers list other examples of deceptive AI behavior. GPT-4, OpenAI’s latest large language model, came up with lies during a test in which it was prompted to persuade a human to solve a CAPTCHA for it. The system also dabbled in insider trading during a simulated exercise in which it was told to assume the identity of a pressurized stock trader, despite never being specifically instructed to do so.

The fact that an AI model has the potential to behave in a deceptive manner without any direction to do so may seem concerning. But it mostly arises from the “ black box” problem that characterizes state-of-the-art machine-learning models: it is impossible to say exactly how or why they produce the results they do—or whether they’ll always exhibit that behavior going forward, says Peter S. Park, a postdoctoral fellow studying AI existential safety at MIT, who worked on the project. 

“Just because your AI has certain behaviors or tendencies in a test environment does not mean that the same lessons will hold if it’s released into the wild,” he says. “There’s no easy way to solve this—if you want to learn what the AI will do once it’s deployed into the wild, then you just have to deploy it into the wild.”

Our tendency to anthropomorphize AI models colors the way we test these systems and what we think about their capabilities. After all, passing tests designed to measure human creativity doesn’t mean AI models are actually being creative. It is crucial that regulators and AI companies carefully weigh the technology’s potential to cause harm against its potential benefits for society and make clear distinctions between what the models can and can’t do, says Harry Law, an AI researcher at the University of Cambridge, who did not work on the research.“These are really tough questions,” he says.

Fundamentally, it’s currently impossible to train an AI model that’s incapable of deception in all possible situations, he says. Also, the potential for deceitful behavior is one of many problems—alongside the propensity to amplify bias and misinformation—that need to be addressed before AI models should be trusted with real-world tasks. 

Artificial intelligence

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