Using Distance Estimates in Heuristic Search

Abstract

This paper explores the use of an oft-ignored information source in heuristic search: a search-distance-to-go estimate. Operators frequently have different costs and cost-to-go is not the same as search-distance-to-go. We evaluate two previous proposals: dynamically weighted A* and A* epsilon. We present a revision to dynamically weighted A* that improves its performance substantially in domains where the search does not progress uniformly towards solutions, and particularly in certain temporal planning problems. We show how to incorporate distance estimates into weighted A* and improve its performance in several domains. Both approaches lead to dramatic performance increases in popular benchmark domains.