Fully revised and updated, this third edition includes three new chapters on neural networks and deep learning including generative AI, causality, and the social, ethical and regulatory impacts of artificial intelligence. All parts have been updated with the methods that have been proven to work. The book's novel agent design space provides a coherent framework for learning, reasoning and decision making. Numerous realistic applications and examples facilitate student understanding. Every concept or algorithm is presented in pseudocode and open source AIPython code, enabling students to experiment with and build on the implementations. Five larger case studies are developed throughout the book and connect the design approaches to the applications. Each chapter now has a social impact section, enabling students to understand the impact of the various techniques as they learn them. An invaluable teaching package for undergraduate and graduate AI courses, this comprehensive textbook is accompanied by lecture slides, solutions, and code.
About the Author
David L. Poole is Professor of Computer Science at the University of British Columbia. He is a former chair of the Association for Uncertainty in Artificial Intelligence, the winner of the Canadian AI Association (CAIAC) Lifetime Achievement Award, and is a Fellow of AAAI and CAIAC. Alan K. Mackworth is a Professor Emeritus of Computer Science at the University of British Columbia, where he co-founded the pioneering UBC Cognitive Systems Program. He served as President of CAIAC, IJCAII, and AAAI, and now acts as a consultant, writer and lecturer. He is a Fellow of AAAI, CAIAC, CIFAR, AGE-WELL and the Royal Society of Canada.
Industry Reviews
'This is an important textbook. Based on their broad experience, the authors harmonize some of the most exciting recent developments in the field, such as generative AI, with more traditional methods, within a unified agent framework. This will broaden the perspective of those relatively new to the field, for whom AI and deep learning appear almost synonymous.' Yoav Shoham, Stanford University and AI21 Labs
'This book is a tour de force. It provides a comprehensive introduction to so many topics in modern AI. The clarity of the exposition and the ability to capture the intuition underlying complex concepts make this book compelling and appealing to a broad audience.' Pascal Van Hentenryck, Georgia Institute of Technology
'This new edition offers an up-to-date account of AI, presenting the field in an accessible and unified manner. I particularly like the 'relations-late' approach, in which first-order logic and relational AI are covered later, after thoroughly covering more basic, feature-based methods. The hybrid data-driven/model-based approach to agent design that the authors propose will be essential to the development of reliable and trustworthy intelligent systems.' Kevin Patrick Murphy, Google Brain, author of Probabilistic Machine Learning
'Poole and Mackworth's now classic textbook has guided my senior undergraduate AI class since its first edition. Coupled with online resources, the book presents a comprehensive overview, with technical substance and many pointers for further study, in a coherent structure that fosters learning of key interrelated concepts. The third edition updates the content to cover the massive recent AI advances.' Jesse Hoey, University of Waterloo
'Machine learning has undergone spectacular advances over the last few years, but to harvest the new capabilities one needs an engineering framework to build computational agents. This book teaches students about the concepts and techniques that make that possible.' Rodney Brooks, MIT and Robust AI
'Wide-ranging, well-organized, up-to-date, and in-depth coverage of the AI world. The numerous figures, algorithms, and extensive references make this a valuable resource that readers will return to repeatedly. Instructors and students will benefit from the well-crafted end-of-chapter exercises. The thought-provoking social impact sections in each chapter and the social impact chapter admirably address the positive and harmful impacts on people. These complement the strong technical descriptions, wisely encouraging researchers and practitioners to limit the risks by highlighting human-centred AI. Poole and Mackworth are highly acclaimed experts who eagerly present their subject with enthusiasm and thoroughness.' Ben Shneiderman, University of Maryland, author of Human-Centered AI
'This revised and extended edition of Artificial Intelligence: Foundations of Computational Agents should become the standard text of AI education. Computer science students will find in this volume a broad and uniquely coherent perspective on many computational models of learning, reasoning, and decision-making. Students of causal inference, in particular, will rejoice at viewing the causal revolution reconnected to its roots in formal logic and probabilistic decision-making, strengthened and reinforced by concrete algorithms, challenging exercises, and open source AIPython codes. Highly recommended.' Judea Pearl, UCLA, Turing Award winner and author of Causality and The Book of Why
'This textbook is impressively comprehensive, covering all the major AI paradigms that have been introduced and studied over the years. At the same time, it is up to date with the latest technical advances and interdisciplinary perspectives on social impacts. I expect it to be a valuable resource for both teachers and students.' Peter Stone, University of Texas at Austin
