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Machine Learning Proceedings 1991 - 1st Edition - ISBN: 9781558602007, 9781483298177

Machine Learning Proceedings 1991

1st Edition

Proceedings of the Eighth International Workshop (ML91)

Editors: Lawrence A. Birnbaum Gregg C. Collins
eBook ISBN: 9781483298177
Imprint: Morgan Kaufmann
Published Date: 1st June 1991
Page Count: 661
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Machine Learning: Proceedings of the Eighth International Workshop (ML91) covers the papers presented at ML91, the Eighth International Workshop on Machine Learning, held at Northwestern University, Evanston, Illinois, USA, in June 1991. The book focuses on constructive induction, learning from theory and data, automated knowledge acquisition, learning in intelligent information retrieval, machine learning in engineering automation, computational models of human learning, and learning reaction strategies. The selection first offers information on design rationale capture as knowledge acquisition, a domain-independent framework for effective experimentation in planning, and knowledge refinement using a high-level, non-technical vocabulary. The text then elaborates on improving the performance of inconsistent knowledge bases via combined optimization method, flexibility of speculative refinement, and a prototype based symbolic concept learning system. Topics include using task descriptions to generate error candidates, functional descriptions of knowledge-based systems, combined optimization method, and inconsistency and related work. The book ponders on learning words from context, modeling the acquisition and improvement of motor skills, a computational model of acquisition for children's addition strategies, and computer modeling of acquisition orders in child language. The manuscript also takes a look at knowledge acquisition combining analytical and empirical techniques; designing integrated learning systems for engineering design; and machine learning for nondestructive evaluation. The selection is highly recommended for researchers interested in machine learning.

Table of Contents

Part I Automated Knowledge Acquisition

Design Rationale Capture as Knowledge Acquisition

A Domain-Independent Framework for Effective Experimentation in Planning

Knowledge Refinement Using a High Level, Non-Technical Vocabulary

Improving the Performance of Inconsistent Knowledge Bases Via Combined Optimization Method

The Flexibility of Speculative Refinement

Generating Error Candidates for Assigning Blame in a Knowledge Base

Part II Computational Models of Human Learning

A Prototype Based Symbolic Concept Learning System

Combining Evidence of Deep and Surface Similarity

The Importance of Causal Structure and Facts in Evaluating Explanations

Learning Words From Context

Modeling the Acquisition and Improvement of Motor Skills

A Computational Model of Acquisition for Children's Addition Strategies

Internal World Models and Supervised Learning

Babel: A Psychologically Plausible Cross-Linguistic Model of Lexical and Syntactic Acquisition

The Acquisition of Human Planning Expertise

Adaptive Pattern-Oriented Chess

Variability Bias and Category Learning

A Constraint-Motivated Model of Lexical Acquisition

Computer Modeling of Acquisition Orders in Child Language

Simulating Stages of Human Cognitive Development With Connectionist Models

Learning Physics Via Explanation-Based Learning of Correctness and Analogical Search Control

Part III Constructive Induction

Incremental Constructive Induction: An Instance-Based Approach

A Transformational Approach to Constructive Induction

Learning Variable Descriptors for Applying Heuristics Across CSP Problems

Informed Pruning in Constructive Induction

A Hybrid Method for Feature Generation

Abstracting Concepts with Inverse Resolution

Opportunistic Constructive Induction

Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning

Discovering Production Rules with Higher Order Neural Networks

Constructive Induction on Symbolic Features

Comparison of Methods Based on Inverse Resolution

The Need for Constructive Induction

Constructive Induction in Theory Refinement

Constructive Induction of M-of-N Terms

Relations, Knowledge and Empirical Learning

Learning Concepts by Synthesizing Minimal Threshold Gate Networks

On the Effect of Instance Representation on Generalization

Relational Clichés: Constraining Constructive Induction During Relational Learning

Learning Polynomial Functions by Feature Construction

Constructive Induction in Knowledge-Based Neural Networks

Feature Construction in Structural Decision Trees

Fringe-Like Feature Construction: A Comparative Study and a Unifying Scheme

A Neural Network Approach to Constructive Induction

Part IV Learning in Intelligent Information Retrieval

Learning in Intelligent Information Retrieval

A Probabilistic Retrieval Scheme for Cluster-Based Adaptive Information Retrieval

Classification Trees for Information Retrieval

Query Formulation Through Knowledge Acquisition

Incremental Learning in a Probabilistic Information Retrieval System

Query Learning Using an ANN with Adaptive Architecture

A Goal-Based Approach to Intelligent Information Retrieval

Machine Learning in the Combination of Expert Opinion Approach to IR

Predicting Actions from Induction on Past Performance

Part V Learning Reaction Strategies

Decision-Theoretic Learning in an Action System

On Becoming Decreasingly Reactive: Learning to Deliberate Minimally

Learning the Persistence of Actions in Reactive Control Rules

Learning to Avoid Obstacles Through Reinforcement

Learning Footfall Evaluation for a Walking Robot

The Blind Leading the Blind: Mutual Refinement of Approximate Theories

Learning to Select a Model in a Changing World

Learning from Deliberated Reactivity

Self-Improvement Based on Reinforcement Learning, Planning and Teaching

Scaling Reinforcement Learning to Robotics by Exploiting the Subsumption Architecture

Variable Resolution Dynamic Programming

Learning a Set of Primitive Actions with an Uninterpreted Sensorimotor Apparatus

Incremental Development of Complex Behaviors

Transfer of Learning Across Compositions of Sequential Tasks

Planning by Incremental Dynamic Programming

Learning a Cost-Sensitive Internal Representation for Reinforcement Learning

Complexity and Cooperation in Q-Learning

Scaling Reinforcement Learning Techniques via Modularity

Part VI Learning Relations

Probabilistic Concept Formation in Relational Domains

Experiments in Non-Monotonic Learning

Learning Qualitative Models of Dynamic Systems

An Investigation of Noise-Tolerant Relational Concept Learning Algorithms

Integrity Constraints and Interactive Concept-Learning

Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL

Inducing Temporal Fault Diagnostic Rules from a Qualitative Model

Learning Spatial Relations from Images

Using Inverse Resolution to Learn Relations from Experiments

Efficient Learning of Logic Programs with Non-Determinant, Non-Discriminating Literals

Learning Search Control Rules for Planning: An Inductive Approach

Learning Constrained Atoms

A Knowledge-Intensive Approach to Learning Relational Concepts

The Consistent Concept Axiom

Determinate Literals in Inductive Logic Programming

First-Order Theory Revision

Completeness for Inductive Procedures

Constraints on Predicate Invention

Revising Relational Domain Theories

Learning Stochastic Motifs from Genetic Sequences

Part VII Learning From Theory and Data

Refinement of Approximate Reasoning-Based Controllers by Reinforcement Learning

Improving Learning Using Causality and Abduction

The DUCTOR: A Theory Revision System for Propositional Domains

The Generality of Overgenerality

Probabilistic Evaluation of Bias for Learning Systems

Incremental Refinement of Approximate Domain Theories

An Enhancer for Reactive Plans

A Hybrid Approach to Guaranteed Effective Control Strategies

Revision Cost for Theory Refinement

Revision of Reduced Theories

Refining Domain Theories Expressed as Finite-State Automata

A Smallest Generalization Step Strategy

Improving Shared Rules in Multiple Category Domain Theories

Discovering Regularities from Large Knowledge Bases

Learning with Inscrutable Theories

A Method for Multistrategy Task-Adaptive Learning Based on Plausible Justifications

Using Background Knowledge in Concept Formation

A Study of How Domain Knowledge Improves Knowledge-Based Learning Systems

Is it a Pocket or a Purse? Tightly Coupled Theory and Data Driven Learning

Identifying Cost Effective Boundaries of Operationally

Part VIII Machine Learning in Engineering Automation

Machine Learning In Engineering Automation

Noise-Resistant Classification

Comparing Stochastic Planning to the Acquisition of Increasingly Permissive Plans

Conceptual Clustering and Exploratory Data Analysis

Megainduction: a Test Flight

Knowledge Compilation to Speed Up Numerical Optimization

Model Revision: A Theory of Incremental Model Learning

Learning Analytical Knowledge About VLSI-Design from Observation

Continuous Conceptual Set Covering: Learning Robot Operators From Examples

Machine Learning for Nondestructive Evaluation

Improving Recognition Effectiveness of Noisy Texture Concepts

Knowledge-Based Equation Discovery in Engineering Domains

Designing Integrated Learning Systems for Engineering Design

Database Consistency Via Inductive Learning

AIMS: An Adaptive Interactive Modeling System for Supporting Engineering Decision Making

Decision Tree Induction of 3-D Manufacturing Features

Part IX Addendum

Knowledge Acquisition Combining Analytical and Empirical Techniques


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© Morgan Kaufmann 1991
1st June 1991
Morgan Kaufmann
eBook ISBN:

About the Editors

Lawrence A. Birnbaum

Gregg C. Collins

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