Principles of Artificial Intelligence (eBook)

Nils J. Nilsson (Autor)

eBook Download: PDF

2014 | 1. Auflage
476 Seiten
Elsevier Science (Verlag)
978-1-4832-9586-2 (ISBN)

Infos

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.

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.

Inhalt

Front Cover 1
Principles of Artificial Intelligence 4
Copyright Page 5
Table of Contents 8
PREFACE 12
ACKNOWLEDGEMENTS 14
CREDITS 16
PROLOGUE 18
0.1. Some Applications of Artificial Intelligence 19
0.2. Overview 26
0.3. Bibliographical and Historical Remarks 27
CHAPTER 1. PRODUCTION SYSTEMS AND AI 34
1.1. Production Systems 34
1.2. Specialized Production Systems 52
1.3. Comments on the Different Types of Production Systems 64
1.4. Bibliographical and Historical Remarks 65
Exercises 67
CHAPTER 2. SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS 70
2.1. Backtracking Strategies 72
2.2. Graph-search Strategies 78
2.3. Uninformed Graph-search Procedures 85
2.4. Heuristic Graph-search Procedures 89
2.5. Related Algorithms 105
2.6. Measures of Performance 108
2.7. Bibliographical and Historical Remarks 111
Exercises 113
CHAPTER 3. SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS 116
3.1. Searching AND/OR Graphs 116
3.2. AO*: A Heuristic Search Procedure for AND/OR Graphs 120
3.3. Some Relationships Between Decomposable and Commutative Systems 126
3.4. Searching Game Trees 129
3.5. Bibliographical and Historical Remarks 144
Exercises 145
CHAPTER 4. THE PREDICATE CALCULUS IN AI 148
4.1. Informal Introduction to the Predicate Calculus 148
4.2. Resolution 162
4.3. The Use of the Predicate Calculus in AI 169
4.4. Bibliographical and Historical Remarks 173
Exercises 173
CHAPTER 5. RESOLUTION REFUTATION SYSTEMS 178
5.1. Production Systems for Resolution Refutations 180
5.2. Control Strategies for Resolution Methods 181
5.3. Simplification Strategies 189
5.4. Extracting Answers From Resolution Refutations 192
5.5. Bibliographical and Historical Remarks 206
Exercises 206
CHAPTER 6. RULE-BASED DEDUCTION SYSTEMS 210
6.1. A Forward Deduction System 213
6.2. A Backward Deduction System 229
6.3. "Resolving" Within AND/OR Graphs 251
6.4. Computation Deductions and Program Synthesis 258
6.5. A Combination Forward and Backward System 270
6.6. Control Knowledge For Rule-Based Deduction Systems 274
6.7. Bibliographical and Historical Remarks 284
Exercises 287
CHAPTER 7. BASIC PLAN-GENERATING SYSTEMS 292
7.1. Robot Problem Solving 292
7.2. A Forward Production System 298
7.3. A Representation for Plans 299
7.4. A Backward Production System 304
7.5. STRIPS 315
7.6. Using Deduction Systems to Generate Robot Plans 324
7.7. Bibliographical and Historical Remarks 332
Exercises 334
CHAPTER 8. ADVANCED PLAN-GENERATING SYSTEMS 338
8.1. RSTRIPS 338
8.2. DCOMP 350
8.3. Amending Plans 359
8.4. Hierarchical Planning 366
8.5. Bibliographical and Historical Remarks 374
Exercises 375
CHAPTER 9. STRUCTURED OBJECT REPRESENTATIONS 378
9.1. From Predicate Calculus to Units 379
9.2. A Graphical Representation: Semantic Networks 387
9.3. Matching 395
9.4. Deductive Operations on Structured Objects 404
9.5 Defaults and Contradictory Information 425
9.6. Bibliographical and Historical Remarks 429
Exercises 431
PROSPECTUS 434
10.1. AI System Architectures 435
10.2. Knowledge Acquisition 436
10.3. Representational Formalisms 439
BIBLIOGRAPHY 446
AUTHOR INDEX 484
SUBJECT INDEX 488

Produkt-Details

• Erscheint lt. Verlag: 28.6.2014
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• ISBN-10: 1-4832-9586-9 / 1483295869
• ISBN-13: 978-1-4832-9586-2 / 9781483295862