Machine Learning Proceedings 1989

Preface

Combining Empirical and Explanation-Based Learning

Unifying Themes in Empirical and Explanation-Based Learning

Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects

Conceptual Clustering of Explanations

A Tight Integration of Deductive and Inductive Learning

Multi-Strategy Learning in Nonhomogeneous Domain Theories

A Description of Preference Criterion in Constructive Learning: A Discussion of Basic Issues

Combining Case-Based Reasoning, Explanation-Based Learning, and Learning from Instruction

Deduction in Top-Down Inductive Learning

One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning

Combining Empirical and Analytical Learning with Version Spaces

Finding New Rules for Incomplete Theories: Explicit Biases for Induction with Contextual Information

Learning from Plausible Explanations

Augmenting Domain Theory for Explanation-Based Generalisation

Explanation Based Learning as Constrained Search

Reducing Search and Learning Goal Preferences

Adaptation-Based Explanation: Explanations as Cases

A Retrieval Model Using Feature Selection

Improving Decision-Making on the Basis of Experience

Explanation-Based Acceleration of Similarity-Based Learning

Knowledge Acquisition Planning: Results and Prospects

"Learning by Instruction" in Connectionist Systems

Integrating Learning in a Neural Network

Explanation-Based Learning with Weak Domain Theories

Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis

A Framework for Improving Efficiency and Accuracy

Error Correction in Constructive Induction

Improving Explanation-Based Indexing with Empirical Learning

A Schema for an Integrated Learning System

Combining Explanation-Based Learning and Artificial Neural Networks

Empirical Learning; Theory and Application

Learning Classification Rules Using Bayes

New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains

Inductive Learning with BCT

What Good Are Experiments?

An Experimental Comparison of Human and Machine Learning Formalisms

Two Algorithms That Learn DNF by Discovering Relevant Features

Limitations on Inductive Learning

The Induction of Probabilistic Rule Sets — The Itrule Algorithm

Empirical Substructure Discovery

Learning the Behavior of Dynamical Systems from Examples

Experiments in Robot Learning

Induction of Decision Trees from Inconclusive Data

Knowledge Intensive Induction

An Ounce of Knowledge is Worth a Ton of Data: Quantitative Studies of the Trade-Off Between Expertise and Data Based On Statistically Well-Founded Empirical Induction

Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning

Unknown Attribute Values in Induction

Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems

Bacon, Data Analysis and Artificial Intelligence

Learning Plan Knowledge

Learning to Plan in Complex Domains

An Empirical Analysis of EBL Approaches for Learning Plan Schemata

Learning Decision Rules for Scheduling Problems: A Classifier Hybrid Approach

Learning Tactical Plans for Pilot Aiding

Issues in the Justification-Based Diagnosis of Planning Failures

Learning Procedural Knowledge in the EBG Context

Learning Invariants from Explanations

Using Learning to Recover Side-Effects of Operators in Robotics

Learning to Recognize Plans Involving Affect

Learning to Retrieve Useful Information for Problem Solving

Discovering Problem Solving Strategies: What Humans Do and Machines Don't (Yet)

Approximating Learned Search Control Knowledge

Planning in Games Using Approximately Learned Macros

Learning Approximate Plans for Use in the Real World

Using Concept Hierarchies to Organize Plan Knowledge

Conceptual Clustering of Mean-Ends Plans

Learning Appropriate Abstractions for Planning in Formation Problems

Discovering Admissible Search Heuristics by Abstracting and Optimizing

Learning Hierarchies of Abstraction Spaces

Learning from Opportunity

Learning by Analyzing Fortuitous Occurrences

Explanation-Based Learning of Reactive Operators

On Becoming Reactive

Knowledge-Base Refinement and Theory Revision

Knowledge Base Refinement and Theory Revision

Theory Formation by Abduction: Initial Results of a Case Study Based on the Chemical Revolution

Using Domain Knowledge to Aid Scientific Theory Revision

The Role of Experimentation in Scientific Theory Revision

Exemplar-Based Theory Rejection: An Approach to the Experience Consistency Problem

Controlling Search for the Consequences of New Information During Knowledge Integration

Identifying Knowledge Base Deficiencies by Observing User Behavior

Toward Automated Rational Reconstruction: A Case Study

Discovering Mathematical Operator Definitions

Imprecise Concept Learning Within a Growing Language

Using Determinations in EBL: A Solution to the Incomplete Theory Problem

Some Results on the Complexity of Knowledge-Base Refinement

Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain Theory

Incremental Learning

Incremental Learning of Control Strategies with Genetic Algorithms

Tower of Hanoi with Connectionist Networks: Learning New Features

A Formal Framework for Learning in Embedded Systems

A Role for Anticipation in Reactive Systems that Learn

Uncertainty Based Selection of Learning Experiences

Improved Training Via Incremental Learning

Incremental Batch Learning

Incremental Concept Formation with Composite Objects

Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms

Focused Concept Formation

An Exploration Into Incremental Learning: The Influence System

Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions

Cost-Sensitive Concept Learning of Sensor Use in Approach and Recognition

Reducing Redundant Learning

Incremental Clustering by Minimizing Representation Length

Information Filters and Their Implementation in the SYLLOG System

Adaptive Learning of Decision-Theoretic Search Control Knowledge

Atoms of Learning II: Adaptive Strategies A Study of Two- Person Zero-Sum Competition

An Incremental Genetic Algorithm for Real-Time Learning

Participatory Learning: A Constructivist Model

Representational Issues in Machine Learning

Representational Issues in Machine Learning

Labor Saving New Distinctions

A Theory of Justified Reformulations

Reformulation from State Space to Reduction Space

Knowledge-Based Feature Generation

Enriching Vocabularies by Generalizing Explanation Structures

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

Towards a Formal Analysis of EBL

A Mathematical Framework for Studying Representation

Refining Representations to Improve Problem Solving Quality

Comparing Systems and Analyzing Functions to Improve Constructive Induction

Evaluating Alternative Instance Representations

Evaluating Bias During Pac-Learning

Constructing Representations Using Inverted Spaces

A Constructive Induction Framework

Constructive Induction by Analogy

Concept Discovery Through Utilization of Invariance Embedded in the Description Language

Declarative Bias for Structural Domains

Automatic Construction of a Hierarchical Generate-and-Test Algorithm

A Knowledge-Level Analysis of Informing

An Object-Oriented Representation for Search Algorithms

Compiling Learning Vocabulary from a Performance System Description

Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts

Screening Hypotheses with Explicit Bias

Building A Learning Bias from Perceived Dependencies

A Bootstrapping Approach to Conceptual Clustering

Overcoming Feature Space Bias in a Reactive Environment

Author Index