In this third edition, the authors have updated the treatment of all major areas. A new organizing principle--the representational dimension of atomic, factored, and structured models--has been added. Significant new material has been provided in partially observable search, contingency planning, hierarchical planning, relational and first-order probability models, regularization and loss functions in machine learning, kernel methods, Web search engines, information extraction, and learning vision and robotics. The book also includes hundreds of new exercises.

Includes Bibliography and Index.

Intelligence agents -- Solving problems by searching -- Beyond classical search -- Adversarial search --Constraint satisfaction problems -- Logical agents -- First-order logic -- Inference in first-order logic -- Classical planning -- Planning and acting in the real world -- Knowledge representation -- Quantifying uncertainty -- Probabilistic reasoning -- Probabilistic reasoning over time -- Making simple decisions -- Making complex decisions -- Learning from examples -- Knowledge in learning -- Learning probabilistic models -- Reinforcement learning -- Natural language processing -- Natural language for communication -- Perception -- Robotics -- Philosophical foundations -- AI: the present and future.