Machine Learning book cover

Machine Learning

An Artificial Intelligence Approach (Volume I)

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.

Hardbound, 572 Pages

Published: January 1955

Imprint: Morgan Kaufmann

ISBN: 978-0-934613-09-5

Contents


  • Preface

    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


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