Artificial Intelligence. The Morgan Kaufmann Series in Artificial Intelligence

  • ID: 1757209
  • Book
  • 513 Pages
  • Elsevier Science and Technology
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Intelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. The book provides a refreshing and motivating new synthesis of the field by one of AI's master expositors and leading researchers. Artificial Intelligence: A New Synthesis takes the reader on a complete tour of this intriguing new world of AI.

  • An evolutionary approach provides a unifying theme
  • Thorough coverage of important AI ideas, old and new
  • Frequent use of examples and illustrative diagrams
  • Extensive coverage of machine learning methods throughout the text
  • Citations to over 500 references
  • Comprehensive index
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1 Introduction
2 Stimulus-Response Agents
3 Neural Networks
4 Machine Evolution
5 State Machines
6 Robot Vision
7 Agents that Plan
8 Uninformed Search
9 Heuristic Search
10 Planning, Acting, and Learning
11 Alternative Search Formulations and Applications
12 Adversarial Search
13 The Propositional Calculus
14 Resolution in The Propositional Calculus
15 The Predicate Calculus
16 Resolution in the Predicate Calculus
17 Knowledge-Based Systems
18 Representing Commonsense Knowledge
19 Reasoning with Uncertain Information
20 Learning and Acting with Bayes Nets
21 The Situation Calculus
22 Planning
23 Multiple Agents
24 Communication Among Agents
25 Agent Architectures
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Nilsson, Nils J.
Nils J. Nilsson's long and rich research career has contributed much to AI. He has written many books, including the classic Principles of Artificial Intelligence. Dr. Nilsson is Kumagai Professor of Engineering, Emeritus, at Stanford University. He has served on the editorial boards of Artificial Intelligence and Machine Learning and as an Area Editor for the Journal of the Association for Computing Machinery. Former Chairman of the Department of Computer Science at Stanford, and former Director of the SRI Artificial Intelligence Center, he is also a past president and Fellow of the American Association for Artificial Intelligence.
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