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.

Bibliographic Information

Book Title: Planning and Learning by Analogical Reasoning
Editors: Manuela M. Veloso
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/3-540-58811-6
Publisher: Springer Berlin, Heidelberg
eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 1994
Softcover ISBN: 978-3-540-58811-5Published: 07 December 1994
eBook ISBN: 978-3-540-49109-5Published: 01 July 2005
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XIV, 190
Topics: Artificial Intelligence, Theory of Computation, Artificial Intelligence

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Planning and Learning by Analogical Reasoning

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