Knowledge Representation and Organization in Machine Learning

✍ Scribed by William R. Swartout, Stephen W. Smoliar (auth.), Katharina Morik (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 1989
• Tongue: English
• Leaves: 333
• Series: Lecture Notes in Computer Science 347 : Lecture Notes in Artificial Intelligence
• Edition: 1
• Category: Library

✦ Synopsis

Machine learning has become a rapidly growing field of Artificial Intelligence. Since the First International Workshop on Machine Learning in 1980, the number of scientists working in the field has been increasing steadily. This situation allows for specialization within the field. There are two types of specialization: on subfields or, orthogonal to them, on special subjects of interest. This book follows the thematic orientation. It contains research papers, each of which throws light upon the relation between knowledge representation, knowledge acquisition and machine learning from a different angle. Building up appropriate representations is considered to be the main concern of knowledge acquisition for knowledge-based systems throughout the book. Here machine learning is presented as a tool for building up such representations. But machine learning itself also states new representational problems. This book gives an easy-to-understand insight into a new field with its problems and the solutions it offers. Thus it will be of good use to both experts and newcomers to the subject.

✦ Table of Contents

Explanation: A source of guidance for knowledge representation....Pages 1-16
(Re)presentation issues in second generation expert systems....Pages 17-49
Some aspects of learning and reorganization in an analogical representation....Pages 50-64
A knowledge-intensive learning system for document retrieval....Pages 65-87
Constructing expert systems as building mental models or toward a cognitive ontology for expert systems ....Pages 88-106
Sloppy modeling....Pages 107-134
The central role of explanations in disciple....Pages 135-147
An inference engine for representing multiple theories....Pages 148-176
The acquisition of model-knowledge for a model-driven machine learning approach....Pages 177-191
Using attribute dependencies for rule learning....Pages 192-210
Learning disjunctive concepts....Pages 211-230
The use of analogy in incremental SBL....Pages 231-246
Knowledge base refinement using apprenticeship learning techniques....Pages 247-257
Creating high level knowledge structures from simple elements....Pages 258-288
Demand-driven concept formation....Pages 289-319

✦ Subjects

Artificial Intelligence (incl. Robotics)