Artificial Intelligence: A Modern Approach (4e) : 9780134610993

Artificial Intelligence: A Modern Approach (4e)

Russell & Norvig
Published by
Pearson Higher Ed USA
Out of stock
Title type
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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.
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
Features & benefits
  • 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.
Student supplements