Table of contents

  1. Front Matter
  2. William R. Swartout, Stephen W. Smoliar
    Pages 1-16
  3. Katharina Morik
    Pages 107-134
  4. Yves Kodratoff, Gheorghe Tecuci
    Pages 135-147
  5. Maarten W. van Someren
    Pages 192-210
  6. Michel Manago, Jim Blythe
    Pages 211-230
  7. Christel Vrain, Yves Kodratoff
    Pages 231-246
  8. Stefan Wrobel
    Pages 289-319

About this book


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.


artificial intelligence expert system inference engine intelligence knowledge base knowledge representation knowledge-based system knowledge-based systems learning machine learning ontology

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 1989
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-50768-0
  • Online ISBN 978-3-540-46081-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site