Michalski / Carbonell / Mitchell Machine Learning

An Artificial Intelligence Approach (Volume I)
1. Auflage 2014
ISBN: 978-0-08-051054-5
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 572 Seiten, Web PDF

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.

Autoren/Hrsg.

Michalski, Ryszard S.

Carbonell, Jaime G.

Mitchell, Tom M.

Inhaltsverzeichnis

1;Front Cover;1
2;Machine Learning: An Artificial Intelligence Approach;4
3;Copyright Page;5
4;Table of Contents;8
5;PREFACE;6
6;PART ONE: GENERAL ISSUES IN MACHINE LEARNING;14
6.1;Chapter 1. An Overview of Machine Learning;16
6.1.1;1.1 Introduction;16
6.1.2;1.2 The Objectives of Machine Learning;16
6.1.3;1.3 A Taxonomy of Machine Learning Research;20
6.1.4;1.4 An Historical Sketch of Machine Learning;27
6.1.5;1.5 A Brief Reader's Guide;29
6.2;Chapter 2. Why Should Machines Learn?;38
6.2.1;2.1 Introduction;38
6.2.2;2.2 Human Learning and Machine Learning;38
6.2.3;2.3 What is Learning?;41
6.2.4;2.4 Some Learning Programs;43
6.2.5;2.5 Growth of Knowledge in Large Systems;45
6.2.6;2.6 A Role for Learning;47
6.2.7;2.7 Concluding Remarks;48
7;PART TWO: LEARNING FROM EXAMPLES;52
7.1;Chapter 3. A Comparative Review of Selected Methods for Learning from Examples;54
7.1.1;3.1 Introduction;54
7.1.2;3.2 Comparative Review of Selected Methods;62
7.1.3;3.3 Conclusion;88
7.2;Chapter 4. A Theory and Methodology of Inductive Learning;96
7.2.1;4.1 Introduction;96
7.2.2;4.2 Types of Inductive Learning;100
7.2.3;4.3 Description Language;107
7.2.4;4.4 Problem Background Knowledge;109
7.2.5;4.5 Generalization Rules;116
7.2.6;4.6 The Star Methodology;125
7.2.7;4.7 An Example;129
7.2.8;4.8 Conclusion;136
7.2.9;4.A Annotated Predicate Calculus (APC);143
8;PART THREE: LEARNING IN PROBLEM-SOLVING AND PLANNING;148
8.1;Chapter 5. Learning by Analogy: Formulating and Generalizing Plans from Past Experience;150
8.1.1;5.1 Introduction;150
8.1.2;5.2 Problem-Solving by Analogy;152
8.1.3;5.3 Evaluating the Analogical Reasoning Process;162
8.1.4;5.4 Learning Generalized Plans;164
8.1.5;5.5 Concluding Remark;172
8.2;Chapter 6. Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics;176
8.2.1;6.1 Introduction;176
8.2.2;6.2 The Problem;177
8.2.3;6.3 Design of LEX;180
8.2.4;6.4 New Directions: Adding Knowledge to Augment Learning;193
8.2.5;6.5 Summary;202
8.3;Chapter 7. Acquisition of Proof Skills in Geometry;204
8.3.1;7.1 Introduction;204
8.3.2;7.2 A Model of the Skill Underlying Proof Generation;206
8.3.3;7.3 Learning;214
8.3.4;7.4 Knowledge Compilation;215
8.3.5;7.5 Summary of Geometry Learning;230
8.4;Chapter 8. Using Proofs and Refutations to Learn from Experience;234
8.4.1;8.1 Introduction;234
8.4.2;8.2 The Learning Cycle;235
8.4.3;8.3 Five Heuristics for Rectifying Refuted Theories;238
8.4.4;8.4 Computational Problems and Implementation Techniques;247
8.4.5;8.5 Conclusions;251
9;PART FOUR: LEARNING FROM OBSERVATION AND DISCOVERY;254
9.1;Chapter 9. The Role of Heuristics in Learning by Discovery: Three Case Studies;256
9.1.1;9.1 Motivation;256
9.1.2;9.2 Overview;258
9.1.3;9.3 Case Study 1: The AM Program; Heuristics Used to Develop New Knowledge;262
9.1.4;9.4 A Theory of Heuristics;276
9.1.5;9.5 Case Study 2: The Eurisko Program; Heuristics Used to Develop New Heuristics;289
9.1.6;9.6 Heuristics Used to Develop New Representations;295
9.1.7;9.7 Case Study 3: Biological Evolution; Heuristics Used to Generate Plausible Mutations;299
9.1.8;9.8 Conclusions;315
9.2;Chapter 10. Rediscovering Chemistry With the BACON System;320
9.2.1;10.1 Introduction;320
9.2.2;10.2 An Overview of BACON.4;322
9.2.3;10.3 The Discoveries of BACON.4;325
9.2.4;10.4 Rediscovering Nineteenth Century Chemistry;332
9.2.5;10.5 Conclusions;339
9.3;Chapter 11. Learning From Observation: Conceptual Clustering;344
9.3.1;11.1 Introduction;345
9.3.2;11.2 Conceptual Cohesiveness;346
9.3.3;11.3 Terminology and Basic Operations of the Algorithm;349
9.3.4;11.4 A Criterion of Clustering Quality;357
9.3.5;11.5 Method and Implementation;358
9.3.6;11.6 An Example of a Practical Problem: Constructing a Classification Hierarchy of Spanish Folk Songs;371
9.3.7;11.7 Summary and Some Suggested Extensions of the Method;373
10;PART FIVE: LEARNING FROM INSTRUCTION;378
10.1;Chapter 12. Machine Transformation of Advice into a Heuristic Search Procedure;380
10.1.1;12.1 Introduction;380
10.1.2;12.2 Kinds of Knowledge Used;383
10.1.3;12.3 A Slightly Non-Standard Definition of Heuristic Search;387
10.1.4;12.4 Instantiating the HSM Schema for a Given Problem;391
10.1.5;12.5 Refining HSM by Moving Constraints Between Control Components;397
10.1.6;12.6 Evaluation of Generality;411
10.1.7;12.7 Conclusion;412
10.1.8;12.A Index of Rules;416
10.2;Chapter 13. Learning by Being Told: Acquiring Knowledge for Information Management;418
10.2.1;13.1 Overview;418
10.2.2;13.2 Technical Approach: Experiments with the KLAUS Concept;421
10.2.3;13.3 More Technical Details;426
10.2.4;13.4 Conclusions and Directions for Future Work;431
10.2.5;13.A Training NANOKLAUS About Aircraft Carriers;435
10.3;Chapter 14. The Instructible Production System: A Retrospective Analysis;442
10.3.1;14.1 The Instructive Production System Project;443
10.3.2;14.2 Essential Functional Components of Instructible Systems;449
10.3.3;14.3 Survey of Approaches;456
10.3.4;14.4 Discussion;466
11;PART SIX: APPLIED LEARNING SYSTEMS;474
11.1;Chapter 15. Learning Efficient Classification Procedures and their Application to Chess End Games;476
11.1.1;15.1 Introduction;476
11.1.2;15.2 The Inductive Inference Machinery;478
11.1.3;15.3 The Lost N-ply Experiments;483
11.1.4;15.4 Approximate Classification Rules;487
11.1.5;15.5 Some Thoughts on Discovering Attributes;490
11.1.6;15.6 Conclusion;494
11.2;Chapter 16. Inferring Student Models for Intelligent Computer-Aided Instruction;496
11.2.1;16.1 Introduction;496
11.2.2;16.2 Generating a Complete and Non-redundant Set of Models;501
11.2.3;16.3 Processing Domain Knowledge;516
11.2.4;16.4 Summary;520
11.2.5;16.A An Example of the SELECTIVE Algorithm: LMS-I's Model Generation Algorithm;523
12;Comprehensive Bibliography of Machine Learning;524
13;Glossary of Selected Terms In Machine Learning;564
14;About the Authors;570
15;Author Index;576
16;Subject Index;580