Probabilistic Reasoning in Intelligent Systems (eBook)

Networks of Plausible Inference

Judea Pearl (Autor)

eBook Download: EPUB

2014 | 1. Auflage
552 Seiten
Elsevier Science (Verlag)
978-0-08-051489-5 (ISBN)

Infos

Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Inhalt

Front Cover 1
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference 4
Copyright Page 5
Table of Contents 12
Dedication 6
Preface 8
Chapter 1. UNCERTAINTY IN AI SYSTEMS: AN OVERVIEW 22
1.1 INTRODUCTION 22
1.2 EXTENSIONAL SYSTEMS: MERITS, DEFICIENCIES, AND REMEDIES 25
1.3 INTENSIONAL SYSTEMS AND NETWORK REPRESENTATIONS 33
1.4 THE CASE FOR PROBABILITIES 35
1.5 QUALITATIVE REASONING WITH PROBABILITIES 44
1.6 BIBLIOGRAPHICAL AND HISTORICAL REMARKS 47
Chapter 2. BAYESIAN INFERENCE 50
2.1 BASIC CONCEPTS 50
2.2 HIERARCHICAL MODELING 63
2.3 EPISTEMOLOGICAL ISSUES OF BELIEF UPDATING 73
2.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS 91
Exercises 94
Chapter 3. MARKOV AND BAYESIAN NETWORKS 98
3.1 FROM NUMERICAL TO GRAPHICAL REPRESENTATIONS 99
3.2 MARKOV NETWORKS 117
3.3 BAYESIAN NETWORKS 137
3.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS 152
Exercises 155
APPENDIX 3-A Proof of Theorem 3 160
APPENDIX 3-B Proof of Theorem 4 162
Chapter 4. BELIEF UPDATING BY NETWORK PROPAGATION 164
4.1 AUTONOMOUS PROPAGATION AS A COMPUTATIONAL PARADIGM 165
4.2 BELIEF PROPAGATION IN CAUSAL TREES 171
4.3 BELIEF PROPAGATION IN CAUSAL POLYTREES (SINGLY CONNECTED NETWORKS) 196
4.4 COPING WITH LOOPS 216
4.5 WHAT ELSE CAN BAYESIAN NETWORKS COMPUTE? 244
4.6 BIBLIOGRAPHICAL AND HISTORICAL REMARKS 253
Exercises 255
APPENDIX 4-A Auxilliary Derivations for Section 4.5.3 257
Chapter 5. DISTRIBUTED REVISION OF COMPOSITE BELIEFS 260
5.1 INTRODUCTION 260
5.2 ILLUSTRATING THE PROPAGATION SCHEME 262
5.3 BELIEF REVISION IN SINGLY CONNECTED NETWORKS 271
5.4 DIAGNOSIS OF SYSTEMS WITH MULTIPLE FAULTS 284
5.5 APPLICATION TO MEDICAL DIAGNOSIS 293
5.6 THE NATURE OF EXPLANATIONS 302
5.7 CONCLUSIONS 307
5.8 BIBLIOGRAPHICAL AND HISTORICAL REMARKS 308
Exercises 309
Chapter 6. DECISION AND CONTROL 310
6.1 FROM BELIEFS TO ACTIONS: INTRODUCTION TO DECISION ANALYSIS 310
6.2 DECISION TREES AND INFLUENCE DIAGRAMS 320
6.3 THE VALUE OF INFORMATION 334
6.4 RELEVANCE-BASED CONTROL 339
6.5 BIBLIOGRAPHICAL AND HISTORICAL REMARKS 348
Exercises 349
Chapter 7. TAXONOMIC HIERARCHIES, CONTINUOUS VARIABLES, AND UNCERTAIN PROBABILITIES 354
7.1 EVIDENTIAL REASONING IN TAXONOMIC HIERARCHIES 354
7.2 MANAGING CONTINUOUS VARIABLES 365
7.3 REPRESENTING UNCERTAINTY ABOUT PROBABILITIES 378
7.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS 393
Exercises 395
APPENDIX 7-A Derivation of Propagation Rules For Continuous Variables 396
Chapter 8. LEARNING STRUCTURE FROM DATA 402
8.1 CAUSALITY, MODULARITY, AND TREE STRUCTURES 404
8.2 STRUCTURING THE OBSERVABLES 408
8.3 LEARNING HIDDEN CAUSE 419
8.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS 429
EXERCISES 430
APPENDIX 8-A Proof of Theorems 1 and 2 432
APPENDIX 8-B Conditions for Star-Decomposability (After Lazarfeld [1966]) 433
Chapter 9. NON-BAYESIAN FORMALISMS FOR MANAGING UNCERTAINTY 436
9.1 THE DEMPSTER-SHAFER THEORY 437
9.2 TRUTH MAINTENANCE SYSTEMS 471
9.3 PROBABILISTIC LOGIC 478
9.4 BIBLIOGRAPHICAL AND HISTORICAL REMARKS 483
Exercises 486
Chapter 10. LOGIC AND PROBABILITY: THE STRANGE CONNECTION 488
10.1 INTRODUCTION TO NONMONOTONIC REASONING 488
10.2 PROBABILISTIC SEMANTICS FOR DEFAULT REASONING 502
10.3 EMBRACING CAUSALITY IN DEFAULT REASONING 518
10.4 A PROBABILISTIC TREATMENT OF THE YALE SHOOTING PROBLEM 530
10.5 BIBLIOGRAPHICAL AND HISTORICAL REMARKS 537
Exercises 539
Bibliography 542
Author Index 560
Subject Index 566

Produkt-Details

• Erscheint lt. Verlag: 28.6.2014
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• ISBN-10: 0-08-051489-8 / 0080514898
• ISBN-13: 978-0-08-051489-5 / 9780080514895