Table of contents

  1. Front Matter
  2. Pages 1-13
  3. Pages 15-32
  4. Pages 33-52
  5. Pages 111-139
  6. Pages 141-162
  7. Pages 163-168
  8. Pages 169-172
  9. Back Matter

About this book


This research monograph describes the integration of analogical and case-based reasoning into general problem solving and planning as a method of speedup learning. The method, based on derivational analogy, has been fully implemented in PRODIGY/ANALOGY and proven in practice to be amenable to scaling up, both in terms of domain and problem complexity.
In this work, the strategy-level learning process is cast for the first time as the automation of the complete cycle of construction, storing, retrieving, and flexibly reusing problem solving experience. The algorithms involved are presented in detail and numerous examples are given. Thus the book addresses researchers as well as practitioners.


Analoges Schließen Analogical Reasoning Fallbasiertes Schließen Machine Learning Maschinelles Lernen Planning Planungsstrategien case-based reasoning complexity learning problem solving

Bibliographic information

  • Book Title Planning and Learning by Analogical Reasoning
  • Authors Manuela M. Veloso
  • Series Title Lecture Notes in Computer Science
  • Series Abbreviated Title Lect Notes Comput Sci
  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 1994
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Softcover ISBN 978-3-540-58811-5
  • eBook ISBN 978-3-540-49109-5
  • Series ISSN 0302-9743
  • Series E-ISSN 1611-3349
  • Edition Number 1
  • Number of Pages XIV, 190
  • Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
  • Topics Artificial Intelligence
    Theory of Computation
    Artificial Intelligence
  • Buy this book on publisher's site
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