Artificial intelligence : a modern approach

By: Russell, Stuart J [Author]Contributor(s): Norvig, PeterMaterial type: TextTextPublication details: New Delhi : Pearson Education, ©1995Description: xxviii, 932 p. : illustrationsISBN: 0131038052 ; 9780131038059 ; 0133601242 ; 9780133601244; 8178085542Subject(s): Artificial intelligenceDDC classification: 006.3
Contents:
I. 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.
Summary: Intelligent 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.
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Item type Current library Collection Call number Status Date due Barcode Item holds
Reference Books Reference Books Main Library
Reference 		Reference 006.3 RUS (Browse shelf(Opens below)) Checked out 25/10/2023 008246
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Includes Bibliography, Index.

I. 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.

Intelligent 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.

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