Example-guided optimization of recursive domain theories

Ronen Feldman*, Devika Subramanian

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

The authors investigate the utility of explanation-based learning in recursive domain theories and examine the cost of using macro-rules in these theories. As a first step in producing effective explanation-based generalization (EBG) algorithms, the authors present a new algorithm for performing source optimization of recursive domain theories. The algorithm, RSG (recursive-structure generalizer), uses a training example as bias and generalizes the control knowledge encoded in the example's derivation tree to produce a more efficient formulation of the original domain theory. The control knowledge involves control of both clause and binding selection. The authors demonstrate the effectiveness of the method of planning problems in situation calculus. The authors show that in most cases one must know the future problem distribution a priori to produce an optimal reformulation.

Original languageAmerican English
Title of host publicationProceedings of the Conference on Artificial Intelligence Applications
PublisherPubl by IEEE
Pages240-244
Number of pages5
ISBN (Print)0818621354
StatePublished - Feb 1990
Externally publishedYes
EventProceedings of the 7th IEEE Conference on Artificial Intelligence Applications - Miami Beach, FL, USA
Duration: 24 Feb 199128 Feb 1991

Publication series

NameProceedings of the Conference on Artificial Intelligence Applications

Conference

ConferenceProceedings of the 7th IEEE Conference on Artificial Intelligence Applications
CityMiami Beach, FL, USA
Period24/02/9128/02/91

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