Full text of second edition now available

The full text is now freely available with stable links.

You can pre-order a copy and instructors can request an examination copy from Cambridge University Press

The first edition is still available.

Artificial Intelligence: Foundations of Computational Agents, second edition, Cambridge University Press, 2017, 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 an 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.

You can search the book and the website:

  • The complete book is available online. This html will be stable and will only change when errors are found.
  • 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.
    • AIPython: Python implementations of most of the pseudo-code, that is designed to be as close to the pseudo-code as possible and to be useful.
    • 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 relational probabilistic reasoning.
    • There are many algorithm demos including applets demonstrating robot localization, decision theoretic planning, reinforcement learning, learning to coordinate and Bayesian learning.
    • We have some prototype interactive tutorials on search, CSPs, and causality with more to come.
  • Slides are available for teaching.
  • We also have a (partial) solution manual you can get from David if you can prove you are teaching from the book.