Artificial intelligence : a modern approach /
Stuart J. Russell and Peter Norvig.
- Third edition, global edition.
- Essex : Pearson, 2016.
- xviii, 1132 p. : ill. b&w ; 25 cm.
Includes bibliographical references and index.
Pt. 1. Artificial intelligence -- Ch. 1. Introduction -- Ch. 2. Intelligent agents -- Pt. 2. Problem-solving -- Ch. 3. Solving problems by searching -- Ch. 4. Beyond classical search -- Ch. 5. Adversarial search -- Ch. 6. Constraint satisfaction problems -- Pt. 3. Knowledge, reasoning, and planning -- Ch. 7. Logical agents -- Ch. 8. First-order logic -- Ch. 9. Inference in first-order logic -- Ch. 10. Classical planning -- Ch. 11. Planning and acting in the real world -- Ch. 12. Knowledge representation -- Pt. 4. Uncertain knowledge and reasoning -- Ch. 13. Quantifying uncertainty -- Ch. 14. Probabilistic reasoning -- Ch. 15. Probabilistic reasoning over time -- Ch. 16. Making simple decisions -- Ch. 17. Making complex decisions -- Pt. 5. Learning -- Ch. 18. Learning from examples -- Ch. 19. Knowledge in learning -- Ch. 20. Learning probabilistic models -- Ch. 21. Reinforcement learning -- Pt. 6. Communicating, perceiving, and acting -- Ch. 22. Natural language processing -- Ch. 23. Natural language for communication -- Ch. 24. Perception -- Ch. 25. Robotics -- Pt. 7. Conclusions -- Ch. 26. Philosophical foundations -- Ch. 27. AI: The present and future -- A. Mathematical background -- B. Notes on language and algorithms.
For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.
This edition captures the changes in AI that have taken place since the last edition in 2003. There have been important applications of AI technology, such as the widespread deployment of practical speech recognition, machine translation, autonomous vehicles, and household robotics. There have been algorithmic landmarks, such as the solution of the game of checkers. And there has been a great deal of theoretical progress, particularly in areas such as probabilistic reasoning, machine learning, and computer vision. Most important from the authors' point of view is the continued evolution in how we think about the field, and thus how the book is organized.