Ebook: Machine Learning: A Guide to Current Research

One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.

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Content:
Front Matter....Pages i-xv
Judge: A Case-based Reasoning System....Pages 1-4
Changing Language while Learning Recursive Descriptions from Examples....Pages 5-9
Learning by Disjunctive Spanning....Pages 11-14
Transfer of Knowledge Between Teaching and Learning Systems....Pages 15-18
Some Approaches to Knowledge Acquisition....Pages 19-24
Analogical Learning with Multiple Models....Pages 25-28
The World Modelers Project: Objectives and Simulator Architecture....Pages 29-34
The Acquisition of Procedural Knowledge through Inductive Learning....Pages 35-38
Learning Static Evaluation Functions by Linear Regression....Pages 39-42
Plan Invention and Plan Transformation....Pages 43-45
A Brief Overview of Explanatory Schema Acquisition....Pages 47-50
The EG Project: Recent Progress....Pages 51-54
Learning Causal Relations....Pages 55-58
Functional Properties and Concept Formation....Pages 59-62
Explanation-Based Learning in Logic Circuit Design....Pages 63-66
A Proposed Method of Conceptual Clustering for Structured and Decomposable Objects....Pages 67-70
Exploiting Functional Vocabularies to Learn Structural Descriptions....Pages 71-74
Combining Numeric and Symbolic Learning Techniques....Pages 75-80
Learning by Understanding Analogies....Pages 81-84
Analogical Reasoning in the Context of Acquiring Problem Solving Expertise....Pages 85-88
Planning and Learning in a Design Domain: The Problems Plan Interactions....Pages 89-92
Inference of Incorrect Operators....Pages 93-97
A Conceptual Framework for Concept Identification....Pages 99-102
Neural Modeling as one Approach to Machine Learning....Pages 103-108
Steps Toward Building a Dynamic Memory....Pages 109-113
Learning by Composition....Pages 115-117
Knowledge Acquisition: Investigations and General Principles....Pages 119-121
Purpose-Directed Analogy: A Summary of Current Research....Pages 123-126
Development of a Framework for Contextual Concept Learning....Pages 127-131
On Safely Ignoring Hypotheses....Pages 133-136
A Model of Acquiring Problem Solving Expertise....Pages 137-140
Another Learning Problem: Symbolic Process Prediction....Pages 141-144
Learning at LRI Orsay....Pages 145-150
Coper: A Methodology for Learning Invariant Functional Descriptions....Pages 151-154
Using Experience as a Guide for Problem Solving....Pages 155-159
Heuristics as Invariants and its Application to Learning....Pages 161-165
Components of Learning in a Reactive Environment....Pages 167-171
The Development of Structures Through Interaction....Pages 173-177
Complex Learning Environments: Hierarchies and the Use of Explanation....Pages 179-182
Prediction and Control in an Active Environment....Pages 183-187
Better Information Retrieval Through Linguistic Sophistication....Pages 189-192
Machine Learning Research in the Artificial Intelligence Laboratory at Illinois....Pages 193-197
Overview of the Prodigy Learning Apprentice....Pages 199-202
A Learning Apprentice System for VLSI Design....Pages 203-206
Generalizing Explanations of Narratives into Schemata....Pages 207-212
Why are Design Derivations Hard to Replay?....Pages 213-218
An Architecture for Experiential Learning....Pages 219-226
Knowledge Extraction Through Learning from Examples....Pages 227-231
Learning Concepts with a Prototype-Based Model for Concept Representation....Pages 233-236
Recent Progress on the Mathematician’s Apprentice Project....Pages 237-240
Acquiring Domain Knowledge from Fragments of Advice....Pages 241-245
Calm: Contestation for Argumentative Learning Machine....Pages 247-253
Directed Experimentation for Theory Revision and Conceptual Knowledge Acquisition....Pages 255-259
Goal-free Learning by Analogy....Pages 261-267
A Scientific Approach To Practical Induction....Pages 269-274
Exploring Shifts of Representation....Pages 275-279
Current Research on Learning in Soar....Pages 281-290
Learning Concepts in a Complex Robot World....Pages 291-294
Learning Evaluation Functions....Pages 295-297
Learning from Data with Errors....Pages 299-302
Explanation-Based Manipulator Learning....Pages 303-306
Learning Classical Physics....Pages 307-310
Views and Causality in Discovery: Modelling Human Induction....Pages 311-315
Learning Control Information....Pages 317-319
An Investigation of the Nature of Mathematical Discovery....Pages 321-326
Learning How to Reach a Goal: A Strategy for the Multiple Classes Classification Problem....Pages 327-332
Conceptual Clustering of Structured Objects....Pages 333-336
Learning in Intractable Domains....Pages 337-341
On Compiling Explainable Models of a Design Domain....Pages 343-347
What can be Learned?....Pages 349-351
Learning Heuristic Rules from Deep Reasoning....Pages 353-357
Learning a Domain Theory by Completing Explanations....Pages 359-362
Learning Implementation Rules with Operating-Conditions Depending on Internal Structures in VLSI Design....Pages 363-368
Overview of the Odysseus Learning Apprentice....Pages 369-373
Learning from Exceptions in Databases....Pages 375-377
Learning Apprentice Systems Research at Schlumberger....Pages 379-383
Language Acquisition: Learning Phrases in Context....Pages 386-389
Back Matter....Pages 391-429

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Content:
Front Matter....Pages i-xv
Judge: A Case-based Reasoning System....Pages 1-4
Changing Language while Learning Recursive Descriptions from Examples....Pages 5-9
Learning by Disjunctive Spanning....Pages 11-14
Transfer of Knowledge Between Teaching and Learning Systems....Pages 15-18
Some Approaches to Knowledge Acquisition....Pages 19-24
Analogical Learning with Multiple Models....Pages 25-28
The World Modelers Project: Objectives and Simulator Architecture....Pages 29-34
The Acquisition of Procedural Knowledge through Inductive Learning....Pages 35-38
Learning Static Evaluation Functions by Linear Regression....Pages 39-42
Plan Invention and Plan Transformation....Pages 43-45
A Brief Overview of Explanatory Schema Acquisition....Pages 47-50
The EG Project: Recent Progress....Pages 51-54
Learning Causal Relations....Pages 55-58
Functional Properties and Concept Formation....Pages 59-62
Explanation-Based Learning in Logic Circuit Design....Pages 63-66
A Proposed Method of Conceptual Clustering for Structured and Decomposable Objects....Pages 67-70
Exploiting Functional Vocabularies to Learn Structural Descriptions....Pages 71-74
Combining Numeric and Symbolic Learning Techniques....Pages 75-80
Learning by Understanding Analogies....Pages 81-84
Analogical Reasoning in the Context of Acquiring Problem Solving Expertise....Pages 85-88
Planning and Learning in a Design Domain: The Problems Plan Interactions....Pages 89-92
Inference of Incorrect Operators....Pages 93-97
A Conceptual Framework for Concept Identification....Pages 99-102
Neural Modeling as one Approach to Machine Learning....Pages 103-108
Steps Toward Building a Dynamic Memory....Pages 109-113
Learning by Composition....Pages 115-117
Knowledge Acquisition: Investigations and General Principles....Pages 119-121
Purpose-Directed Analogy: A Summary of Current Research....Pages 123-126
Development of a Framework for Contextual Concept Learning....Pages 127-131
On Safely Ignoring Hypotheses....Pages 133-136
A Model of Acquiring Problem Solving Expertise....Pages 137-140
Another Learning Problem: Symbolic Process Prediction....Pages 141-144
Learning at LRI Orsay....Pages 145-150
Coper: A Methodology for Learning Invariant Functional Descriptions....Pages 151-154
Using Experience as a Guide for Problem Solving....Pages 155-159
Heuristics as Invariants and its Application to Learning....Pages 161-165
Components of Learning in a Reactive Environment....Pages 167-171
The Development of Structures Through Interaction....Pages 173-177
Complex Learning Environments: Hierarchies and the Use of Explanation....Pages 179-182
Prediction and Control in an Active Environment....Pages 183-187
Better Information Retrieval Through Linguistic Sophistication....Pages 189-192
Machine Learning Research in the Artificial Intelligence Laboratory at Illinois....Pages 193-197
Overview of the Prodigy Learning Apprentice....Pages 199-202
A Learning Apprentice System for VLSI Design....Pages 203-206
Generalizing Explanations of Narratives into Schemata....Pages 207-212
Why are Design Derivations Hard to Replay?....Pages 213-218
An Architecture for Experiential Learning....Pages 219-226
Knowledge Extraction Through Learning from Examples....Pages 227-231
Learning Concepts with a Prototype-Based Model for Concept Representation....Pages 233-236
Recent Progress on the Mathematician’s Apprentice Project....Pages 237-240
Acquiring Domain Knowledge from Fragments of Advice....Pages 241-245
Calm: Contestation for Argumentative Learning Machine....Pages 247-253
Directed Experimentation for Theory Revision and Conceptual Knowledge Acquisition....Pages 255-259
Goal-free Learning by Analogy....Pages 261-267
A Scientific Approach To Practical Induction....Pages 269-274
Exploring Shifts of Representation....Pages 275-279
Current Research on Learning in Soar....Pages 281-290
Learning Concepts in a Complex Robot World....Pages 291-294
Learning Evaluation Functions....Pages 295-297
Learning from Data with Errors....Pages 299-302
Explanation-Based Manipulator Learning....Pages 303-306
Learning Classical Physics....Pages 307-310
Views and Causality in Discovery: Modelling Human Induction....Pages 311-315
Learning Control Information....Pages 317-319
An Investigation of the Nature of Mathematical Discovery....Pages 321-326
Learning How to Reach a Goal: A Strategy for the Multiple Classes Classification Problem....Pages 327-332
Conceptual Clustering of Structured Objects....Pages 333-336
Learning in Intractable Domains....Pages 337-341
On Compiling Explainable Models of a Design Domain....Pages 343-347
What can be Learned?....Pages 349-351
Learning Heuristic Rules from Deep Reasoning....Pages 353-357
Learning a Domain Theory by Completing Explanations....Pages 359-362
Learning Implementation Rules with Operating-Conditions Depending on Internal Structures in VLSI Design....Pages 363-368
Overview of the Odysseus Learning Apprentice....Pages 369-373
Learning from Exceptions in Databases....Pages 375-377
Learning Apprentice Systems Research at Schlumberger....Pages 379-383
Language Acquisition: Learning Phrases in Context....Pages 386-389
Back Matter....Pages 391-429
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