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Artificial Intelligence: A Modern Approach, 4th edition

Published by Pearson (April 28, 2020) © 2021

  • Stuart Russell University of California at Berkeley
  • Peter Norvig

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The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence
The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
  • Nontechnical learning material introduces major concepts using intuitive explanations, before going into mathematical or algorithmic details. The nontechnical language makes the book accessible to a broader range of readers.
  • A unified approach to AI shows students how the various subfields of AI fit together to build actual, useful programs.
  • The basic definition of AI systems is generalised to eliminate the standard assumption that the objective is fixed and known by the intelligent agent; instead, the agent may be uncertain about the true objectives of the human(s) on whose behalf it operates.
  • In-depth coverage of both basic and advanced topics provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.
  • Stay current with the latest technologies and present concepts in a more unified manner
  • New chapters feature expanded coverage of probabilistic programming (Ch. 15); multiagent decision making (Ch. 18 with Michael Wooldridge); deep learning (Ch. 21 with Ian Goodfellow); and deep learning for natural language processing (Ch. 24 with Jacob Devlin and Mei-Wing Chang).
  • Increased coverage of machine learning.
  • Significantly updated material on robotics includes robots that interact with humans and the application of reinforcement learning to robotics.
  • New section on causality by Judea Pearl.
  • New sections on Monte Carlo search for games and robotics.
  • New sections on transfer learning for deep learning in general and for natural language.
  • New sections on privacy, fairness, the future of work, and safe AI.
  • Extensive coverage of recent advances in AI applications.
  • Revised coverage of computer vision, natural language understanding, and speech recognition reflect the impact of deep learning methods on these fields.
Offer the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence
  • The basic definition of AI systems is generalized to eliminate the standard assumption that the objective is fixed and known by the intelligent agent; instead, the agent may be uncertain about the true objectives of the human(s) on whose behalf it operates.
  • The Author-Maintained Website at http://aima.cs.berkeley.edu/ includes text-related comments and discussions, exercises, an online code repository, Instructor Resources, and more!
    • Interactive student exercises are now featured on the website to allow for continuous updating and additions.
    • Updated online software gives students more opportunities to complete projects, including implementations of the algorithms in the book, plus supplemental coding examples and applications in Python, Java, and Javascript.
    • New instructional video tutorials deepen students’ engagement and bring key concepts to life.
Stay current with the latest technologies and present concepts in a more unified manner
  • New chapters feature expanded coverage of probabilistic programming (Ch. 15); multiagent decision making (Ch. 18 with Michael Wooldridge); deep learning (Ch. 21 with Ian Goodfellow); and deep learning for natural language processing (Ch. 24 with Jacob Devlin and Mei-Wing Chang).
  • Increased coverage of machine learning.
  • Significantly updated material on robotics includes robots that interact with humans and the application of reinforcement learning to robotics.
  • New section on causality by Judea Pearl.
  • New sections on Monte Carlo search for games and robotics.
  • New sections on transfer learning for deep learning in general and for natural language.
  • New sections on privacy, fairness, the future of work, and safe AI.
  • Extensive coverage of recent advances in AI applications.
  • Revised coverage of computer vision, natural language understanding, and speech recognition reflect the impact of deep learning methods on these fields.

Brief Table of Contents

  1. Introduction
  2. Intelligent Agents
  3. Solving Problems by Searching
  4. Search in Complex Environments
  5. Adversarial Search and Games
  6. Constraint Satisfaction Problems
  7. Logical Agents
  8. First-Order Logic
  9. Inference in First-Order Logic
  10. Knowledge Representation
  11. Automated Planning
  12. Quantifying Uncertainty
  13. Probabilistic Reasoning
  14. Probabilistic Reasoning over Time
  15. Probabilistic Programming
  16. Making Simple Decisions
  17. Making Complex Decisions
  18. Multiagent Decision Making
  19. Learning from Examples
  20. Learning Probabilistic Models
  21. Deep Learning
  22. Reinforcement Learning
  23. Natural Language Processing
  24. Deep Learning for Natural Language Processing
  25. Robotics
  26. Philosophy and Ethics of AI
  27. The Future of AI

About our authors

Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor and former chair of computer science, director of the Center for Human-Compatible AI, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300 papers on a wide range of topics in artificial intelligence. His other books include: The Use of Knowledge in Analogy and Induction, Do the Right Thing: Studies in Limited Rationality (with Eric Wefald), and Human Compatible: Artificial Intelligence and the Problem of Control.

Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA’s research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and a research faculty member at Berkeley. His other books are: Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.

The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.

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