Abstract

This article outlines explanation-based learning (EBL) and its role in improving problem solving performance through experience. Unlike inductive systems, which learn by abstracting common properties from multiple examples, EBL systems explain why a particular example is an instance of a concept. The explanations are then converted into operational recognition rules. In essence, the EBL approach is analytical and knowledge-intensive, whereas inductive methods are empirical and knowledge-poor. This article focuses on extensions of the basic EBL method and their integration with the prodigy problem solving system. prodigy's EBL method is specifically designed to acquire search control rules that are effective in reducing total search time for complex task domains. Domain-specific search control rules are learned from successful problem solving decisions, costly failures, and unforeseen goal interactions …

Date

September 1, 1989

Authors

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

Journal

Artificial Intelligence

Volume

40

Issue

1-3

Pages

63-118

Publisher

Elsevier