Case-Based Reasoning (eBook)

Janet Kolodner (Autor)

eBook Download: PDF

2014 | 1. Auflage
612 Seiten
Elsevier Science (Verlag)
978-1-4832-9449-0 (ISBN)

Infos

Case-based reasoning is one of the fastest growing areas in the field of knowledge-based systems and this book, authored by a leader in the field, is the first comprehensive text on the subject. Case-based reasoning systems are systems that store information about situations in their memory. As new problems arise, similar situations are searched out to help solve these problems. Problems are understood and inferences are made by finding the closest cases in memory, comparing and contrasting the problem with those cases, making inferences based on those comparisons, and asking questions when inferences can't be made. This book presents the state of the art in case-based reasoning. The author synthesizes and analyzes a broad range of approaches, with special emphasis on applying case-based reasoning to complex real-world problem-solving tasks such as medical diagnosis, design, conflict resolution, and planning. The author's approach combines cognitive science and engineering, and is based on analysis of both expert and common-sense tasks. Guidelines for building case-based expert systems are provided, such as how to represent knowledge in cases, how to index cases for accessibility, how to implement retrieval processes for efficiency, and how to adapt old solutions to fit new situations. This book is an excellent text for courses and tutorials on case-based reasoning. It is also a useful resource for computer professionals and cognitive scientists interested in learning more about this fast-growing field.

Inhalt

Front Cover 1
Case-Based Reasoning 2
Copyright Page 3
Table of Contents 6
Preface 14
Part I: Background 20
Chapter 1. What Is Case-Based Reasoning? 22
1.1 Introduction 22
1.2 What Is a Case? 27
1.3 Major CBR Issues: Composition and Specificity 33
1.4 Processes and Issues 35
1.5 Applicability of Case-Based Reasoning 42
1.6 Cognitive Model, or Methodology for Building Expert Systems? 46
1.7 A Note to Readers 48
1.8 Summary 49
Chapter 2. Case Studies of Several Case-Based Reasoners 52
2.1 CHEF 53
2.2 CASEY 59
2.3 JULIA 62
2.4 HYPO 67
2.5 PROTOS 70
2.6 CLAVIER 74
2.7 Retrieval-Only Aiding and Advisory Systems 79
2.8 Summary 90
Chapter 3. Reasoning Using Cases 92
3.1 Case-Based Inference 93
3.2 CBR and Problem Solving 96
3.3 Interpretive CBR 105
3.4 Case-Based and Other Reasoning Methods 111
3.5 Summary 116
Chapter 4. The Cognitive Model 118
4.1 A Short Intellectual History 118
4.2 Dynamic Memory 124
4.3 Beyond Intentional Situations: Dynamic Memory and Model-Based Reasoning 135
4.4 Some Running Cognitive Models 139
4.5 Summary of Claims 152
4.6 Evidence of Case-Based Reasoning in People and Its Implications 156
Part II: The Case Library: Representing and Indexing Cases 160
Chapter 5. Representing Cases 164
5.1 Component Parts of Cases 165
5.2 The Issue of Case Presentation 179
5.3 Case Studies 182
5.4 Advanced Issues 199
5.5 Summary 209
Chapter 6. Indexing Vocabulary 212
6.1 Qualities of Good Indexes 217
6.2 Choosing Vocabulary 221
6.3 Toward a Generally Applicable Indexing Vocabulary 238
6.4 The Universal Index Frame: A Vocabulary for Intentional Situations 240
6.5 Generally Applicable Indexing Schemes: Lessons Illustrated by the UIF 257
6.6 Beyond the Universal Index Frame 262
6.7 Summary 264
Chapter 7. Methods for Index Selection 266
7.1 Choosing Indexes by Hand 268
7.2 Choosing Indexes by Machine 276
7.3 Choosing Indexes Based on a Checklist 276
7.4 Difference-Based Indexing 285
7.5 Combining Difference-Based and Checklist-Based Methods 285
7.6 Explanation-Based Indexing 287
7.7 Combining Explanation-Based, Checklist-Based, and Difference-Based Methods 297
7.8 Choosing an Automated Indexing Method 297
7.9 Summary 299
Part III: Retrieving Cases from the Case Library 302
Chapter 8. Organizational Structures and Retrieval Algorithms 308
8.1 A Note About Matching 310
8.2 A Set of Cases 311
8.3 Flat Memory, Serial Search 312
8.4 Hierarchical Organizations of Cases: Shared Feature Networks 314
8.5 Discrimination Networks 319
8.6 A Major Disadvantage 322
8.7 Redundant Discrimination Networks 322
8.8 Flat Library, Parallel Search 328
8.9 Hierarchical Memory, Parallel Search 331
8.10 Discussion 333
8.11 Summary 339
Chapter 9. Matching and Ranking Cases 340
9.1 Some Definitions 344
9.2 The Building Blocks of Matching and Ranking Processes 349
9.3 Putting It All Together 372
9.4 Summary 386
Chapter 10. Indexing and Retrieval 388
10.1 Situation Assessment: Choosing Indexes for Retrieval 390
10.2 Implementing Indexes 402
10.3 Achieving Efficiency, Accuracy, and Flexibility 403
10.4 Summary 407
Part IV: Using Cases 410
Chapter 11. Adaptation Methods and Strategies 412
11.1 Substitution 416
11.2 Transformation 437
11.3 Special-Purpose Adaptation and Repair Heuristics 450
11.4 Derivational Replay 454
11.5 Summary 455
Chapter 12. Controlling Adaptation 458
12.1 Identifying What Needs To Be Fixed 459
12.2 Choosing an Adaptation Strategy 473
12.3 Choosing What Gets Adapted and the Method of Adaptation in Tandem 479
12.4 Flow of Control 482
12.5 Summary 486
Chapter 13. Using Cases for Interpretation and Evaluation 488
13.1 Exemplar-based Classification 494
13.2 Case-Based Interpretation 501
13.3 Critiquing Solutions: Case-Based Projection 514
13.4 Summary 521
Chapter 14. Using Cases: Some Additional Issues 524
14.1 Using Reasoning Goals to Guide Case-Based Processes 526
14.2 Anticipating Potential Problems and Opportunities for Enhancement 532
14.3 Deriving Subgoals 535
14.4 Types of Reasoning Goals and Tasks 536
14.5 Goal Scheduling 538
14.6 Integrating the Goal Scheduler With the Case-Based Reasoner 540
14.7 When to Use a Goal Scheduler 540
14.8 A Neglected Complexity: Merging Pieces of Several Solutions 541
14.9 Summary 543
Part V: Pulling It All Together 546
Chapter 15. Building a Case-Based Reasoner 548
15.1 First Things First: When Should a Case-Based Reasoner Be Used? 551
15.2 Which Tasks and Subtasks Should the Case-Based Reasoner Support? 556
15.3 What Degree of Automation Should Be Used? 559
15.4 Building and Maintaining the Case Library 562
15.5 Maintaining the Case Library 566
15.6 Case Presentation and Human-Computer Interaction 575
15.7 Summary 580
Chapter 16. Conclusions, Opportunities, Challenges 582
16.1 Case-Based Reasoning And Learning 584
16.2 Conclusions 587
16.3 Challenges And Opportunities 590
16.4 The Future 598
Appendix: A Case Library of Case-Based Reasoning Systems 600
Bibliography 648
Index 670

Produkt-Details

• Erscheint lt. Verlag: 28.6.2014
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• ISBN-10: 1-4832-9449-8 / 1483294498
• ISBN-13: 978-1-4832-9449-0 / 9781483294490