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Description 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. top Contents I. ARTIFICIAL INTELLIGENCE. 1. Introduction. 2. Intelligent Agents. II. PROBLEM-SOLVING. 3. Solving Problems by Searching. 4. Informed Search and Exploration. 5. Constraint Satisfaction Problems. 6. Adversarial Search. III. KNOWLEDGE AND REASONING. 7. Logical Agents. 8. First-Order Logic. 9. Inference in First-Order Logic. 10. Knowledge Representation. IV. PLANNING. 11. Planning. 12. Planning and Acting in the Read World. V. UNCERTAIN KNOWLEDGE AND REASONING. 13. Uncertainty. 14. Probabilistic Reasoning Systems. 15. Probabilistic Reasoning Over Time. 16. Making Simple Decisions. 17. Making Complex Decisions. VI. LEARNING. 18. Learning from Observations. 19. Knowledge in Learning. 20. Statistical Learning Methods. 21. Reinforcement Learning. VII. COMMUNICATING, PERCEIVING, AND ACTING. 22. Agents that Communicate. 23. Text Processing in the Large. 24. Perception. 25. Robotics. VIII. CONCLUSIONS. 26. Philosophical Foundations. 27. AI: Present and Future. top Features NEW - Nontechnical learning material. Provides a simple overview of major concepts, uses a nontechnical language to help increase understanding. Makes the book accessible to a broader range of students. NEW - The Internet as a sample application for intelligent systems—Examples of logical reasoning, planning, and natural language processing using Internet agents. Promotes student interest with interesting, relevant exercises. NEW - Increased coverage of material—New or expanded coverage of constraint satisfaction, local search planning methods, multi-agent systems, game theory, statistical natural language processing and uncertain reasoning over time. More detailed descriptions of algorithms for probabilistic inference, fast propositional inference, probabilistic learning approaches including EM, and other topics. Brings students up to date on the latest technologies, and presents concepts in a more unified manner. NEW - Updated and expanded exercises—75% of the exercises are revised, with 100 new exercises. NEW - More Online Software. Allows many more opportunities for student projects on the web. A unified, agent-based approach to AI—Organizes the material around the task of building intelligent agents. Shows students how the various subfields of AI fit together to build actual, useful programs. Comprehensive, up-to-date coverage—Includes a unified view of the field organized around the rational decision making paradigm. A flexible format. Makes the text adaptable for varying instructors' preferences. In-depth coverage of basic and advanced topics. Provides students with a basic understanding of the frontiers of AI without compromising complexity and depth. Pseudo-code versions of the major AI algorithms are presented in a uniform fashion, and Actual Common Lisp and Python implementations of the presented algorithms are available via the Internet. Gives instructors and students a choice of projects; reading and running the code increases understanding. top Author 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 of computer science, director of the Center for Intelligent Systems, 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 cowinner o