A Structural Theory of Explanation-Based Learning (1992)
| Venue: | Artificial Intelligence |
| Citations: | 50 - 3 self |
BibTeX
@TECHREPORT{Etzioni92astructural,
author = {Oren Etzioni},
title = {A Structural Theory of Explanation-Based Learning},
institution = {Artificial Intelligence},
year = {1992}
}
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Abstract
The impact of Explanation-Based Learning (EBL) on problem-solving efficiency varies greatly from one problem space to another. In fact, seemingly minute modifications to problem space encoding can drastically alter EBL's impact. For example, while prodigy/ebl (a state-of-the-art EBL system) significantly speeds up the prodigy problem solver in the Blocksworld, prodigy/ebl actually slows prodigy down in a representational variant of the Blocksworld constructed by adding a single, carefully chosen, macro-operator to the Blocksworld operator set. Although EBL has been tested experimentally, no theory has been put forth that accounts for such phenomena. This paper presents such a theory. The theory exhibits a correspondence between a graph representation of problem spaces and the proofs used by EBL systems to generate search-control knowledge. The theory relies on this correspondence to account for the variations in EBL's impact. This account is validated by static, a program that extract...
