Machine Learning (eBook)

An Artificial Intelligence Approach (Volume I)

Jaime G. Carbonell, Ryszard S. Michalski, Tom M. Mitchell (Autoren)

eBook Download: PDF

2014 | 1. Auflage
572 Seiten
Elsevier Science (Verlag)
978-0-08-051054-5 (ISBN)

Infos

Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs-particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV and V discuss learning from observation and discovery, and learning from instruction, respectively. Part VI presents two studies on applied learning systems-one on the recovery of valuable information via inductive inference; the other on inducing models of simple algebraic skills from observed student performance in the context of the Leeds Modeling System (LMS). This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers.

Inhalt

Front Cover 1
Machine Learning: An Artificial Intelligence Approach 4
Copyright Page 5
Table of Contents 8
PREFACE 6
PART ONE: GENERAL ISSUES IN MACHINE LEARNING 14
Chapter 1. An Overview of Machine Learning 16
1.1 Introduction 16
1.2 The Objectives of Machine Learning 16
1.3 A Taxonomy of Machine Learning Research 20
1.4 An Historical Sketch of Machine Learning 27
1.5 A Brief Reader's Guide 29
Chapter 2. Why Should Machines Learn? 38
2.1 Introduction 38
2.2 Human Learning and Machine Learning 38
2.3 What is Learning? 41
2.4 Some Learning Programs 43
2.5 Growth of Knowledge in Large Systems 45
2.6 A Role for Learning 47
2.7 Concluding Remarks 48
PART TWO: LEARNING FROM EXAMPLES 52
Chapter 3. A Comparative Review of Selected Methods for Learning from Examples 54
3.1 Introduction 54
3.2 Comparative Review of Selected Methods 62
3.3 Conclusion 88
Chapter 4. A Theory and Methodology of Inductive Learning 96
4.1 Introduction 96
4.2 Types of Inductive Learning 100
4.3 Description Language 107
4.4 Problem Background Knowledge 109
4.5 Generalization Rules 116
4.6 The Star Methodology 125
4.7 An Example 129
4.8 Conclusion 136
4.A Annotated Predicate Calculus (APC) 143
PART THREE: LEARNING IN PROBLEM-SOLVING AND PLANNING 148
Chapter 5. Learning by Analogy: Formulating and Generalizing Plans from Past Experience 150
5.1 Introduction 150
5.2 Problem-Solving by Analogy 152
5.3 Evaluating the Analogical Reasoning Process 162
5.4 Learning Generalized Plans 164
5.5 Concluding Remark 172
Chapter 6. Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics 176
6.1 Introduction 176
6.2 The Problem 177
6.3 Design of LEX 180
6.4 New Directions: Adding Knowledge to Augment Learning 193
6.5 Summary 202
Chapter 7. Acquisition of Proof Skills in Geometry 204
7.1 Introduction 204
7.2 A Model of the Skill Underlying Proof Generation 206
7.3 Learning 214
7.4 Knowledge Compilation 215
7.5 Summary of Geometry Learning 230
Chapter 8. Using Proofs and Refutations to Learn from Experience 234
8.1 Introduction 234
8.2 The Learning Cycle 235
8.3 Five Heuristics for Rectifying Refuted Theories 238
8.4 Computational Problems and Implementation Techniques 247
8.5 Conclusions 251
PART FOUR: LEARNING FROM OBSERVATION AND DISCOVERY 254
Chapter 9. The Role of Heuristics in Learning by Discovery: Three Case Studies 256
9.1 Motivation 256
9.2 Overview 258
9.3 Case Study 1: The AM Program Heuristics Used to Develop New Knowledge
9.4 A Theory of Heuristics 276
9.5 Case Study 2: The Eurisko Program Heuristics Used to Develop New Heuristics
9.6 Heuristics Used to Develop New Representations 295
9.7 Case Study 3: Biological Evolution Heuristics Used to Generate Plausible Mutations
9.8 Conclusions 315
Chapter 10. Rediscovering Chemistry With the BACON System 320
10.1 Introduction 320
10.2 An Overview of BACON.4 322
10.3 The Discoveries of BACON.4 325
10.4 Rediscovering Nineteenth Century Chemistry 332
10.5 Conclusions 339
Chapter 11. Learning From Observation: Conceptual Clustering 344
11.1 Introduction 345
11.2 Conceptual Cohesiveness 346
11.3 Terminology and Basic Operations of the Algorithm 349
11.4 A Criterion of Clustering Quality 357
11.5 Method and Implementation 358
11.6 An Example of a Practical Problem: Constructing a Classification Hierarchy of Spanish Folk Songs 371
11.7 Summary and Some Suggested Extensions of the Method 373
PART FIVE: LEARNING FROM INSTRUCTION 378
Chapter 12. Machine Transformation of Advice into a Heuristic Search Procedure 380
12.1 Introduction 380
12.2 Kinds of Knowledge Used 383
12.3 A Slightly Non-Standard Definition of Heuristic Search 387
12.4 Instantiating the HSM Schema for a Given Problem 391
12.5 Refining HSM by Moving Constraints Between Control Components 397
12.6 Evaluation of Generality 411
12.7 Conclusion 412
12.A Index of Rules 416
Chapter 13. Learning by Being Told: Acquiring Knowledge for Information Management 418
13.1 Overview 418
13.2 Technical Approach: Experiments with the KLAUS Concept 421
13.3 More Technical Details 426
13.4 Conclusions and Directions for Future Work 431
13.A Training NANOKLAUS About Aircraft Carriers 435
Chapter 14. The Instructible Production System: A Retrospective Analysis 442
14.1 The Instructive Production System Project 443
14.2 Essential Functional Components of Instructible Systems 449
14.3 Survey of Approaches 456
14.4 Discussion 466
PART SIX: APPLIED LEARNING SYSTEMS 474
Chapter 15. Learning Efficient Classification Procedures and their Application to Chess End Games 476
15.1 Introduction 476
15.2 The Inductive Inference Machinery 478
15.3 The Lost N-ply Experiments 483
15.4 Approximate Classification Rules 487
15.5 Some Thoughts on Discovering Attributes 490
15.6 Conclusion 494
Chapter 16. Inferring Student Models for Intelligent Computer-Aided Instruction 496
16.1 Introduction 496
16.2 Generating a Complete and Non-redundant Set of Models 501
16.3 Processing Domain Knowledge 516
16.4 Summary 520
16.A An Example of the SELECTIVE Algorithm: LMS-I's Model Generation Algorithm 523
Comprehensive Bibliography of Machine Learning 524
Glossary of Selected Terms In Machine Learning 564
About the Authors 570
Author Index 576
Subject Index 580

Produkt-Details

• Erscheint lt. Verlag: 28.6.2014
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• ISBN-10: 0-08-051054-X / 008051054X
• ISBN-13: 978-0-08-051054-5 / 9780080510545