Artificial Intelligence: A Modern Approach, 4th Edition

Stuart Russell all

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Artificial Intelligence: A Modern Approach, 4th Edition

By Stuart Russell, Peter Norvig
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Stuart Russell all
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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.
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