About the book: 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. There has also been a great deal of theoretical progress, particularly in areas such as probabilistic reasoning, machine learning, and computer vision.

Features

• Nontechnical learning material provides a simple overview of major concepts

• Expanded coverage of topics such as 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
• 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 Internet