Feedback requested

We are currently planning a second edition of the book and are soliciting feedback from instructors, students, and other readers. We would appreciate any feedback you would like to provide, including:

  • What should be explained better?
  • What should be removed or simplified?
  • What should be added? (Is this something that should/would be taught in an undergraduate AI course?)
  • What references should be included?
  • What changes would make the book better?

Please email David and Alan any feedback you may have.


Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, 2010, is a book about the science of artificial intelligence (AI). It presents artificial intelligence as the study of the design of intelligent computational agents. The book is structured as a textbook, but it is accessible to a wide audience of professionals and researchers. In the last decades we have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. This book provides the first accessible synthesis of the field aimed at undergraduate and graduate students. It provides a coherent vision of the foundations of the field as it is today. It aims to provide that synthesis as an integrated science, in terms of a multi-dimensional design space that has been partially explored. As with any science worth its salt, artificial intelligence has a coherent, formal theory and a rambunctious experimental wing. The book balances theory and experiment, showing how to link them intimately together. It develops the science of AI together with its engineering applications.

  • The complete book is available online. This html will be stable and will only change when errors are found.
  • We have a list of errata from the first printing.
  • We have many online learning resources for many of the topics of this book. The book is closely coordinated with AIspace : tools for learning Artificial Intelligence. Many of the examples in the book are animated by the AIspace tools.
  • There are many algorithm demos including applets demonstrating robot localization, decision theoretic planning, reinforcement learning, learning to coordinate and Bayesian learning.
  • Slides are available for teaching.
  • We have some prototype interactive tutorials on search, CSPs, and causality with more to come.
  • AILog is a representation and reasoning system with declarative debugging and explanation tools that implements many of the logical representations in the book, including negation-as-failure, abduction and probabilistic reasoning.
  • We also have a (partial) solution manual you can get from David if you can prove you are teaching from the book!

We are requesting feedback on errors for this edition and suggestions for subsequent editions. Please email any comments to the authors. We appreciate feedback on references that we are missing (particularly good recent surveys), attributions that we should have made, what could be explained better, where we need more or better examples, topics that we should cover in more or less detail (although we are reluctant to add more topics; we'd rather explain fewer topics in more detail), topics that could be omitted, as well as typos. This is meant to be a textbook, not a summary of (recent) research.