Machine Learning - 1st Edition - ISBN: 9780934613095, 9780080510545

Machine Learning

1st Edition

An Artificial Intelligence Approach (Volume I)

Authors: Ryszard Michalski Jaime Carbonell Tom Mitchell
Hardcover ISBN: 9780934613095
eBook ISBN: 9780080510545
Imprint: Morgan Kaufmann
Published Date: 1st January 1955
Page Count: 572
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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.

Table of Contents


Part One General Issues in Machine Learning

Chapter 1 An Overview of Machine Learning

1.1 Introduction

1.2 The Objectives of Machine Learning

1.3 A Taxonomy of Machine Learning Research

1.4 An Historical Sketch of Machine Learning

1.5 A Brief Reader's Guide

Chapter 2 Why Should Machines Learn?

2.1 Introduction

2.2 Human Learning and Machine Learning

2.3 What is Learning?

2.4 Some Learning Programs

2.5 Growth of Knowledge in Large Systems

2.6 A Role for Learning

2.7 Concluding Remarks

Part Two Learning from Examples

Chapter 3 A Comparative Review of Selected Methods for Learning from Examples

3.1 Introduction

3.2 Comparative Review of Selected Methods

3.3 Conclusion

Chapter 4 A Theory and Methodology of Inductive Learning

4.1 Introduction

4.2 Types of Inductive Learning

4.3 Description Language

4.4 Problem Background Knowledge

4.5 Generalization Rules

4.6 The Star Methodology

4.7 An Example

4.8 Conclusion

4.A Annotated Predicate Calculus (APC)

Part Three Learning in Problem-Solving and Planning

Chapter 5 Learning by Analogy: Formulating and Generalizing Plans from Past Experience

5.1 Introduction

5.2 Problem-Solving by Analogy

5.3 Evaluating the Analogical Reasoning Process

5.4 Learning Generalized Plans

5.5 Concluding Remark

Chapter 6 Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics

6.1 Introduction

6.2 The Problem

6.3 Design of LEX

6.4 New Directions: Adding Knowledge to Augment Learning

6.5 Summary

Chapter 7 Acquisition of Proof Skills in Geometry

7.1 Introduction

7.2 A Model of the Skill Underlying Proof Generation

7.3 Learning

7.4 Knowledge Compilation

7.5 Summary of Geometry Learning

Chapter 8 Using Proofs and Refutations to Learn from Experience

8.1 Introduction

8.2 The Learning Cycle

8.3 Five Heuristics for Rectifying Refuted Theories

8.4 Computational Problems and Implementation Techniques

8.5 Conclusions

Part Four Learning from Observation and Discovery

Chapter 9 The Role of Heuristics in Learning by Discovery: Three Case Studies

9.1 Motivation

9.2 Overview

9.3 Case Study 1: The AM Program; Heuristics Used to Develop New Knowledge

9.4 A Theory of Heuristics

9.5 Case Study 2: The Eurisko Program; Heuristics Used to Develop New Heuristics

9.6 Heuristics Used to Develop New Representations

9.7 Case Study 3: Biological Evolution; Heuristics Used to Generate Plausible Mutations

9.8 Conclusions

Chapter 10 Rediscovering Chemistry with the BACON System

10.1 Introduction

10.2 An Overview of BACON.4

10.3 The Discoveries of BACON.4

10.4 Rediscovering Nineteenth Century Chemistry

10.5 Conclusions

Chapter 11 Learning from Observation: Conceptual Clustering

11.1 Introduction

11.2 Conceptual Cohesiveness

11.3 Terminology and Basic Operations of the Algorithm

11.4 A Criterion of Clustering Quality

11.5 Method and Implementation

11.6 An Example of a Practical Problem: Constructing a Classification Hierarchy of Spanish Folk Songs

11.7 Summary and Some Suggested Extensions of the Method

Part Five Learning from Instruction

Chapter 12 Machine Transformation of Advice into a Heuristic Search Procedure

12.1 Introduction

12.2 Kinds of Knowledge Used

12.3 A Slightly Non-Standard Definition of Heuristic Search

12.4 Instantiating the HSM Schema for a Given Problem

12.5 Refining HSM by Moving Constraints between Control Components

12.6 Evaluation of Generality

12.7 Conclusion

12.A Index of Rules

Chapter 13 Learning by Being Told: Acquiring Knowledge for Information Management

13.1 Overview

13.2 Technical Approach: Experiments with the KLAUS Concept

13.3 More Technical Details

13.4 Conclusions and Directions for Future Work

13.A Training NANOKLAUS about Aircraft Carriers

Chapter 14 The Instructive Production System: A Retrospective Analysis

14.1 The Instructive Production System Project

14.2 Essential Functional Components of Instructive Systems

14.3 Survey of Approaches

14.4 Discussion

Part Six Applied Learning Systems

Chapter 15 Learning Efficient Classification Procedures and Their Application to Chess End Games

15.1 Introduction

15.2 The Inductive Inference Machinery

15.3 The Lost N-ply Experiments

15.4 Approximate Classification Rules

15.5 Some Thoughts on Discovering Attributes

15.6 Conclusion

Chapter 16 Inferring Student Models for Intelligent Computer-Aided Instruction

16.1 Introduction

16.2 Generating a Complete and Non-redundant Set of Models

16.3 Processing Domain Knowledge

16.4 Summary

16.A An Example of the SELECTIVE Algorithm: LMS-I's Model Generation Algorithm

Comprehensive Bibliography of Machine Learning

Glossary of Selected Terms in Machine Learning

About the Authors

Author Index

Subject Index


No. of pages:
© Morgan Kaufmann 1983
Morgan Kaufmann
eBook ISBN:
Hardcover ISBN:

About the Author

Ryszard Michalski

Affiliations and Expertise

George Mason University

Jaime Carbonell

Tom Mitchell