Breadth-First Heuristic Search

Rong Zhou and Eric A. Hansen

Recent work shows that the memory requirements of best- first heuristic search can be reduced substantially by using a divide-and-conquer method of solution reconstruction. We show that memory requirements can be reduced even further by using a breadth-first instead of a best-first search strategy. We describe optimal and approximate breadth-first heuristic search algorithms that use divide-and-conquer solution reconstruction. Computational results show that they outperform other optimal and approximate heuristic search algorithms in solving domain-independent planning problems.


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