The ability to learn from experience is a fundamental requirement for intelligence. One of the most basic characteristics of human intelligence is that people can learn from problem solving, so that they become more adept at solving problems in a given domain as they gain experience. This book investigates how computers may be programmed so that they too can learn from experience. Specifically, the aim is to take a very general, but inefficient, problem solving system and train it on a set of problems from a given domain, so that it can transform itself into a specialized, efficient problem solver for that domain. on a knowledge-intensive Recently there has been considerable progress made learning approach, explanation-based learning (EBL), that brings us closer to this possibility. As demonstrated in this book, EBL can be used to analyze a problem solving episode in order to acquire control knowledge. Control knowledge guides the problem solver's search by indicating the best alternatives to pursue at each choice point. An EBL system can produce domain specific control knowledge by explaining why the choices made during a problem solving episode were, or were not, appropriate.

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Authors and Affiliations

Carnegie Mellon University, USA

Steven Minton

Bibliographic Information

Book Title: Learning Search Control Knowledge
Book Subtitle: An Explanation-Based Approach
Authors: Steven Minton
Series Title: The Springer International Series in Engineering and Computer Science
DOI: https://doi.org/10.1007/978-1-4613-1703-6
Publisher: Springer New York, NY
eBook Packages: Springer Book Archive
Copyright Information: Kluwer Academic Publishers, Boston 1988
Hardcover ISBN: 978-0-89838-294-5Published: 31 October 1988
Softcover ISBN: 978-1-4612-8960-9Published: 05 October 2011
eBook ISBN: 978-1-4613-1703-6Published: 06 December 2012
Series ISSN: 0893-3405
Edition Number: 1
Number of Pages: X, 214
Topics: Artificial Intelligence

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Table of contents (13 chapters)

Front Matter

Back Matter

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