Towards finding optimal solutions with non-admissible heuristics: a new technique

Authors:
Henry W. Davis;Anna Bramanti-Gregor;Xiaofeng Chen
Affiliations:
Department of Computer Science, Wright State University, Dayton, Ohio;Department of Computer Science, Wright State University, Dayton, Ohio;Department of Computer Science, Wright State University, Dayton, Ohio
Venue:
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Year:
1989

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Abstract

A problem with A* is that it fails to guarantee optimal solutions when its heuristic, h, overestimates. Since optimal solutions are often desired and an underestimating h is not always available, we seek to remedy this. From a nonadmissible h an admissible one is generated using h's statistical properties. The new heuristic, hm, is obtained by inverting h with respect to its own least upper bound function. The set of nodes expanded when A* uses g + hm as an evaluator is compared with the set of nodes expanded using other approaches which have been suggested in the literature. A considerable potential savings in node expansion when using hm is indicated. In 8-puzzle experiments A* using g + hm expands one fifth as many nodes as does the best alternative approach.