3 Review(s)

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. Salient Features 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. 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. 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. Updated and expanded exercises — 30% of the exercises are revised or 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.