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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.
Salient Features
• Nontechnical learning material provides a simple overview of major concepts, uses a nontechnical language to help increase understanding.
• Increased coverage of material like 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
• Updated and expanded exercises — 30% of the exercises are revised or NEW
• A unified, agent-based approach to AI — Organizes the material around the task of building intelligent agents
• Comprehensive, up-to-date coverage — Includes a unified view of the field organized around the rational decision making paradigm
• In-depth coverage of basic and advanced topics which 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 InternetISBN - 9789332518698
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Pages : 1092
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