Machine Learning Proceedings 1991

Machine Learning Proceedings 1991

Proceedings of the Eighth International Workshop (ML91)

1st Edition - June 1, 1991

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  • Editors: Lawrence A. Birnbaum, Gregg C. Collins
  • eBook ISBN: 9781483298177

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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

Product details

  • No. of pages: 661
  • Language: English
  • Copyright: © Morgan Kaufmann 1991
  • Published: June 1, 1991
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9781483298177

About the Editors

Lawrence A. Birnbaum

Gregg C. Collins

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