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E-Book, Englisch, 476 Seiten, Web PDF

Nilsson Principles of Artificial Intelligence


1. Auflage 2014
ISBN: 978-1-4832-9586-2
Verlag: Elsevier Science & Techn.
Format: PDF
 Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 476 Seiten, Web PDF

ISBN: 978-1-4832-9586-2
Verlag: Elsevier Science & Techn.
Format: PDF
 Kopierschutz: 1 - PDF Watermark

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.
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Autoren/Hrsg.

Nilsson, Nils J.

Weitere Infos & Material

1;Front Cover;1
2;Principles of Artificial Intelligence;4
3;Copyright Page;5
4;Table of Contents;8
5;PREFACE;12
6;ACKNOWLEDGEMENTS;14
7;CREDITS;16
8;PROLOGUE;18
8.1;0.1. Some Applications of Artificial Intelligence;19
8.2;0.2. Overview;26
8.3;0.3. Bibliographical and Historical Remarks;27
9;CHAPTER 1. PRODUCTION SYSTEMS AND AI;34
9.1;1.1. Production Systems;34
9.2;1.2. Specialized Production Systems;52
9.3;1.3. Comments on the Different Types of Production Systems;64
9.4;1.4. Bibliographical and Historical Remarks;65
9.5;Exercises;67
10;CHAPTER 2. SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS;70
10.1;2.1. Backtracking Strategies;72
10.2;2.2. Graph-search Strategies;78
10.3;2.3. Uninformed Graph-search Procedures;85
10.4;2.4. Heuristic Graph-search Procedures;89
10.5;2.5. Related Algorithms;105
10.6;2.6. Measures of Performance;108
10.7;2.7. Bibliographical and Historical Remarks;111
10.8;Exercises;113
11;CHAPTER 3. SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS;116
11.1;3.1. Searching AND/OR Graphs;116
11.2;3.2. AO*: A Heuristic Search Procedure for AND/OR Graphs;120
11.3;3.3. Some Relationships Between Decomposable and Commutative Systems;126
11.4;3.4. Searching Game Trees;129
11.5;3.5. Bibliographical and Historical Remarks;144
11.6;Exercises;145
12;CHAPTER 4. THE PREDICATE CALCULUS IN AI;148
12.1;4.1. Informal Introduction to the Predicate Calculus;148
12.2;4.2. Resolution;162
12.3;4.3. The Use of the Predicate Calculus in AI;169
12.4;4.4. Bibliographical and Historical Remarks;173
12.5;Exercises;173
13;CHAPTER 5. RESOLUTION REFUTATION SYSTEMS;178
13.1;5.1. Production Systems for Resolution Refutations;180
13.2;5.2. Control Strategies for Resolution Methods;181
13.3;5.3. Simplification Strategies;189
13.4;5.4. Extracting Answers From Resolution Refutations;192
13.5;5.5. Bibliographical and Historical Remarks;206
13.6;Exercises;206
14;CHAPTER 6. RULE-BASED DEDUCTION SYSTEMS;210
14.1;6.1. A Forward Deduction System;213
14.2;6.2. A Backward Deduction System;229
14.3;6.3. "Resolving" Within AND/OR Graphs;251
14.4;6.4. Computation Deductions and Program Synthesis;258
14.5;6.5. A Combination Forward and Backward System;270
14.6;6.6. Control Knowledge For Rule-Based Deduction Systems;274
14.7;6.7. Bibliographical and Historical Remarks;284
14.8;Exercises;287
15;CHAPTER 7. BASIC PLAN-GENERATING SYSTEMS;292
15.1;7.1. Robot Problem Solving;292
15.2;7.2. A Forward Production System;298
15.3;7.3. A Representation for Plans;299
15.4;7.4. A Backward Production System;304
15.5;7.5. STRIPS;315
15.6;7.6. Using Deduction Systems to Generate Robot Plans;324
15.7;7.7. Bibliographical and Historical Remarks;332
15.8;Exercises;334
16;CHAPTER 8. ADVANCED PLAN-GENERATING SYSTEMS;338
16.1;8.1. RSTRIPS;338
16.2;8.2. DCOMP;350
16.3;8.3. Amending Plans;359
16.4;8.4. Hierarchical Planning;366
16.5;8.5. Bibliographical and Historical Remarks;374
16.6;Exercises;375
17;CHAPTER 9. STRUCTURED OBJECT REPRESENTATIONS;378
17.1;9.1. From Predicate Calculus to Units;379
17.2;9.2. A Graphical Representation: Semantic Networks;387
17.3;9.3. Matching;395
17.4;9.4. Deductive Operations on Structured Objects ;404
17.5;9.5 Defaults and Contradictory Information;425
17.6;9.6. Bibliographical and Historical Remarks;429
17.7;Exercises;431
18;PROSPECTUS;434
18.1;10.1. AI System Architectures;435
18.2;10.2. Knowledge Acquisition;436
18.3;10.3. Representational Formalisms;439
19;BIBLIOGRAPHY;446
20;AUTHOR INDEX;484
21;SUBJECT INDEX;488

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