Abstract

In order to solve problems more effectively with accumulating experience, a system must be able to learn and exploit search control knowledge. While previous research has demonstrated that explanation-based learning is a viable method for acquiring search control knowledge, in practice explanation-based techniques may not generate effective control knowledge. For control knowledge to be effective, the cumulative benefits of applying the knowledge must outweigh the cumulative costs of testing to see whether the knowledge is applicable. To produce effective control knowledge, an explanation-based learner must generate explanations that capture the key features relevant to control decisions, and represent this information so that it can be easily taken advantage of. This paper describes three mechanisms incorporated in the PRODIGY system for attacking this problem. First, PRODIGY is selective about what …

Date

January 1, 1987

Authors

Steven Minton, Jaime G Carbonell, Oren Etzioni, Craig A Knoblock, Daniel R Kuokka

Book

Proceedings of the fourth International workshop on Machine Learning

Pages

122-133

Publisher

Morgan Kaufmann