Principles of Artificial Intelligence
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A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.
Table of Contents
- Principles of Artificial Intelligence
by Nils Nilsson- Preface
Acknowledgements
Credits
Prologue- 0.1 Some Applications of Artificial Intelligence
0.2 Overview
0.3 Bibliography and Historical Remarks
CHAPTER 1: PRODUCTION SYSTEMS AND AI- 1.1 Production Systems
1.2 Specialized Production Systems
1.3 Comments on the Different Types of Production Systems
1.4 Bibliographical and Historical Remarks
Exercises
CHAPTER 2: SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS- 2.1 Backtracking Strategies
2.2 Graph-search Strategies
2.3 Ununiformed Graph-search Procedures
2.4 Heuristic Graph-search Procedures
2.5 Related Algorithms
2.6 Measures of Performance
2.7 Bibliographical and Historical Remarks
Exercises
CHAPTER 3: SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS- 3.1 Searching AND/OR Graphs
3.2 AO*: A Heuristic Search Procedure for AND/OR Graphs
3.3 Some Relationships Between Decomposable and Commutative Systems
3.4 Searching Game Trees
3.5 Bibliographical and Historical Remarks
Exercises
CHAPTER 4: THE PREDICATE CALCULUS IN AI- 4.1 Informal Introduction to the Predicate Calculus
4.2 Resolution
4.3 The Use of the Predicate Calculus in AI
4.4 Bibliographical and Historical Remarks
Exercises
CHAPTER 5: RESOLUTION REFUTATION SYSTEMS- 5.1 Production Systems for Resolution Refutations
5.2 Control Strategies for Resolution Methods
5.3 Simplification Strategies
5.4 Extracting Answers From Resolution Refutations
5.5 Bibliographical and Historical Remarks
Exercises
CHAPTER 6: Rule-Based Deduction Systems- 6.1 A Forward Deduction System
6.2 A Backward Deduction System
6.3 "Resolving" Within AND/OR Graphs
6.4 Computation Deductions and Program Synthesis
6.5 A Combination Forward and Backward System
6.6 Control Knowledge For Rule-Based Deduction Systems
6.7 Bibliographical and Historical Remarks
Exercises
CHAPTER 7: BASIC PLAN-GENERATING SYSTEMS- 7.1 Robot Problem Solving
7.2 A Forward Production System
7.3 A Representation for Plans
7.4 A Backward Preoduction System
7.5 STRIPS
7.6 Using Deduction Systems to Generate Robot Plans
7.7 Bibliographical and Historical Remarks
Exercises
CHAPTER 8: ADVANCED PLAN-GENERATING SYTEMS- 8.1 RSTRIPS
8.2 DCOMP
8.3 Amending Plans
8.4 Hierarchical Planning
8.5 Bibliographical and Historical Remarks
Exercises
CHAPTER 9: STRUCTURED OBJECT REPRESENTATIONS- 9.1 From Predicate Calculus to Units
9.2 A Graphical Representation: Semantic Networks
9.3 Matching
9.4 Deductive Operations on Structured Objects
9.5 Defaults and Contradictory Information
9.6 Bibliographical and Historical Remarks
Exercises
Prospectus- 10.1 AI System Architectures
10.2 Knowledge Acquisition
10.3 Representational Formalisms
Bibliography
Author Index
Subject Index
Product details
- No. of pages: 476
- Language: English
- Copyright: © Morgan Kaufmann 1982
- Published: February 1, 1982
- Imprint: Morgan Kaufmann
- eBook ISBN: 9781483295862
About the Author
Nils Nilsson
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.
Affiliations and Expertise
Stanford University
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