A* search with inconsistent heuristics

Authors:
Zhifu Zhang;Nathan R. Sturtevant;Robert Holte;Jonathan Schaeffer;Ariel Felner
Affiliations:
Computing Science Department, University of Alberta, Edmonton, Alberta, Canada;Computing Science Department, University of Alberta, Edmonton, Alberta, Canada;Computing Science Department, University of Alberta, Edmonton, Alberta, Canada;Computing Science Department, University of Alberta, Edmonton, Alberta, Canada;Information Systems Engineering, Deutsche Telekom Labs, Ben-Gurion University, Be'er-Sheva, Israel
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

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Abstract

Early research in heuristic search discovered that using inconsistent heuristics with A* could result in an exponential increase in the number of node expansions. As a result, the use of inconsistent heuristics has largely disappeared from practice. Recently, inconsistent heuristics have been shown to be effective in IDA*, especially when applying the bidirectional pathmax (BPMX) enhancement. This paper presents new worst-case complexity analysis of A*'s behavior with inconsistent heuristics, discusses how BPMX can be used with A*, and gives experimental results justifying the use of inconsistent heuristics in A* searches.