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An Introduction to the Planning Domain Definition Language
Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation. The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating ...
An Introduction to the Planning Domain Definition Language
Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation. The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating ...
Genetic Programming
This book constitutes the refereed proceedings of the 25th European Conference on Genetic Programming, EuroGP 2022, held as part of Evo*2021, as Virtual Event, in April 2022, co-located with the Evo*2022 events, EvoCOP, EvoMUSART, and EvoApplications. The 12 revised full papers and 7 short papers presented in this book were carefully reviewed and selected from 35 submissions. The wide range of topics in this volume reflects the current state of research in the field. The collection of papers cover topics including developing new operators for variants of GP algorithms, as well as exploring GP applications to the optimization of machine learning methods and the evolution of complex combinational logic circuits.
Introduction to Logic Programming
Logic Programming is a style of programming in which programs take the form of sets of sentences in the language of Symbolic Logic. Over the years, there has been growing interest in Logic Programming due to applications in deductive databases, automated worksheets, Enterprise Management (business rules), Computational Law, and General Game Playing. This book introduces Logic Programming theory, current technology, and popular applications. In this volume, we take an innovative, model-theoretic approach to logic programming. We begin with the fundamental notion of datasets, i.e., sets of ground atoms. Given this fundamental notion, we introduce views, i.e., virtual relations; and we define c...
Theory and Applications of Satisfiability Testing -- SAT 2015
This book constitutes the refereed proceedings of the 18th International Conference on Theory and Applications of Satisfiability Testing, SAT 2015, held in Austin, TX, USA, in September 2015. The 21 regular papers, 2 short papers and 7 tool papers presented together with 3 invited talks were carefully reviewed and selected from 70 submissions. The papers address different aspects of SAT, including theoretical advances (exact algorithms, proof complexity, and other complexity issues), practical search algorithms, knowledge compilation, implementation-level details of SAT solvers and SAT-based systems, problem encodings and reformulations, and applications, as well as case studies and reports on insightful findings based on rigorous experimentation.The paper 'Constructing SAT Filters with a Quantum Annealer' is published open access under a CC BY-NC 2.5 license at link.springer.com.
Knowledge Engineering Tools and Techniques for AI Planning
This book presents a comprehensive review for Knowledge Engineering tools and techniques that can be used in Artificial Intelligence Planning and Scheduling. KE tools can be used to aid in the acquisition of knowledge and in the construction of domain models, which this book will illustrate. AI planning engines require a domain model which captures knowledge about how a particular domain works - e.g. the objects it contains and the available actions that can be used. However, encoding a planning domain model is not a straightforward task - a domain expert may be needed for their insight into the domain but this information must then be encoded in a suitable representation language. The development of such domain models is both time-consuming and error-prone. Due to these challenges, researchers have developed a number of automated tools and techniques to aid in the capture and representation of knowledge. This book targets researchers and professionals working in knowledge engineering, artificial intelligence and software engineering. Advanced-level students studying AI will also be interested in this book.
Lethal Autonomous Weapons
"Because of the increasing use of Unmanned Aerial Vehicles (UAVs, also commonly known as drones) in various military and para-military (i.e., CIA) settings, there has been increasing debate in the international community as to whether it is morally and ethically permissible to allow robots (flying or otherwise) the ability to decide when and where to take human life. In addition, there has been intense debate as to the legal aspects, particularly from a humanitarian law framework. In response to this growing international debate, the United States government released the Department of Defense (DoD) 3000.09 Directive (2011), which sets a policy for if and when autonomous weapons would be used...
Quantitative Evaluation of Systems
This book constitutes the thoroughly refereed proceedings of the 10th International Conference on Quantitative Evaluation of Systems, QEST 2013, held in Buenos Aires, Argentina, August 27-30, 2013. The 21 revised full papers presented together with 9 tool demonstrations were carefully reviewed and selected from 52 submissions. The papers are organized in topics such as probabilistic automata and Markov automata, population models, model checking and systems, systems, control and games, timed automata and simulation.
Neuro-Symbolic Artificial Intelligence: The State of the Art
Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two hitherto distinct approaches. ”Neuro” refers to the artificial neural networks prominent in machine learning, ”symbolic” refers to algorithmic processing on the level of meaningful symbols, prominent in knowledge representation. In the past, these two fields of AI have been largely separate, with very little crossover, but the so-called “third wave” of AI is now bringing them together. This book, Neuro-Symbolic Artificial Intelligence: The State of the Art, provides an overview of this development in AI. The two approaches differ significantly in terms of their strengths and weaknesses a...
Code to Joy
How we can get more joy from our machines by telling them what our hearts desire. In this informative, accessible, and very funny book, Michael L. Littman inspires readers to learn how to tell machines what to do for us. Rather than give in to the fear that computers will steal our jobs, spy on us and control what we buy and whom we vote for, we can improve our relationship with them just by learning basic programming skills. Our devices will help us, Littman writes, if we can say what we want in a way they can understand. Each chapter of the book focuses on a particular element of what can be said, providing examples of how we use similar communication in our daily interactions with people. Littman offers ways readers can experiment with these ideas right away, using publicly available systems that might also make us more productive as a welcome side effect. Each chapter also reflects on how the use of these programming components can be expedited by machine learning. With humor and teacherly guidance, Code to Joy brings into view a future where programming is like reading—something everyone can learn.
