000			02653nam a2200229 a 4500
999			_c38232 _d38232
020			_a0131038052
020			_a9780131038059
020			_a0133601242
020			_a9780133601244
020			_a8178085542
082			_a006.3 _bRUS
100			_aRussell, Stuart J. ; _eaut
245			_aArtificial intelligence : a modern approach
260			_aNew Delhi : _bPearson Education, _c©1995.
300			_axxviii, 932 p. : _billustrations ;
500			_aIncludes Bibliography, Index.
505			_aI. Artificial Intelligence. Intelligent Agents -- II. Problem-solving. Solving Problems by Searching. Informed Search Methods. Game Playing -- III. Knowledge and reasoning. Agents that Reason Logically. First-Order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems -- IV. Acting logically. Planning. Practical Planning. Planning and Acting -- V. Uncertain knowledge and reasoning. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions -- VI. Learning. Learning from Observations. Learning in Neural and Belief Networks. Reinforcement Learning. Knowledge in Learning -- VII. Communicating, perceiving, and acting. Agents that Communicate. Practical Natural Language Processing. Perception. Robotics -- VIII. Conclusions. Philosophical Foundations. AI: Present and Future -- A Complexity analysis and O() notation -- B Notes on Languages and Algorithms.
520			_aIntelligent Agents - Stuart Russell and Peter Norvig show how intelligent agents can be built using AI methods, and explain how different agent designs are appropriate depending on the nature of the task and environment. Artificial Intelligence: A Modern Approach is the first AI text to present a unified, coherent picture of the field. The authors focus on the topics and techniques that are most promising for building and analyzing current and future intelligent systems. The material is comprehensive and authoritative, yet cohesive and readable. State of the Art - This book covers the most effective modern techniques for solving real problems, including simulated annealing, memory-bounded search, global ontologies, dynamic belief networks, neural networks, adaptive probabilistic networks, inductive logic programming, computational learning theory, and reinforcement learning. Leading edge AI techniques are integrated into intelligent agent designs, using examples and exercises to lead students from simple, reactive agents to advanced planning agents with natural language capabilities.
650			_aArtificial intelligence.
700			_aNorvig, Peter ;
942			_cREF