Enhanced descriptions from Syndetics:

This is an introduction to the theory and practice of artificial intelligence. It uses an intelligent agent as the unifying theme throughout, and covers areas that are sometimes underemphasized elsewhere. These include reasoning under uncertainty, learning, natural language, vision and robotics. The book also explains in detail some of the more recent ideas in the field, including simulated annealing, memory-bounded search, global ontologies, dynamic belief networks, neural nets, inductive logic programming, computational learning theory, and reinforcement learning.

Table of contents provided by Syndetics

• I Artificial Intelligence
• 1 Introduction
• 2 Intelligent Agents
• II Problem-Solving
• 3 Solving Problems by Searching
• 4 Informed Search and Exploration
• 5 Constraint Satisfaction Problems
• 6 Adversarial Search
• III Knowledge And Reasoning
• 7 Logical Agents
• 8 First-Order Logic
• 9 Inference in First-Order Logic
• 10 Knowledge Representation
• IV Planning
• 11 Planning
• 12 Planning and Acting in the Read World
• V Uncertain Knowledge And Reasoning
• 13 Uncertainty
• 14 Probabilistic Reasoning Systems
• 15 Probabilistic Reasoning Over Time
• 16 Making Simple Decisions
• 17 Making Complex Decisions
• VI Learning
• 18 Learning from Observations
• 19 Knowledge in Learning
• 20 Statistical Learning Methods
• 21 Reinforcement Learning
• VII Communicating, Perceiving, And Acting
• 22 Agents that Communicate
• 23 Text Processing in the Large
• 24 Perception
• 25 Robotics
• VIII Conclusions
• 26 Philosophical Foundations
• 27 AI: Present and Future

Reviews provided by Syndetics

CHOICE Review

Russell and Norvig, acknowledged experts in the field, have produced a marvelously clear, exciting, and coherent book on artificial intelligence (AI). Perhaps for the first time, AI is presented as a unified field with the concept of intelligent agents that receive percepts from the environment and perform actions as the unifying theme. All the usual areas of AI are covered, such as reasoning under uncertainty, learning, neural networks, natural language, vision, robotics, and philosophical foundations, as well as the more recent ideas of simulated annealing, memory-bound search, global ontologies, dynamic and adaptive probabilistic networks, computational learning theory, and reinforcement learning. The book gives equal emphasis to theory and practice, includes more than 100 algorithms and 300 exercises, and contains extensive notes and references to the research literature. Overall, an excellent work. Highly recommended. General; upper-division undergraduate through professional. C. Tappert; United States Military Academy