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Breadth-first heuristic search
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| Venue: | Artificial Intelligence |
| Citations: | 55 - 11 self |
BibTeX
@ARTICLE{Zhou_breadth-firstheuristic,
author = {Rong Zhou and Eric A. Hansen},
title = {Breadth-first heuristic search},
journal = {Artificial Intelligence},
year = {},
pages = {385--408}
}
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Abstract
Recent work shows that the memory requirements of bestfirst 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.
Keyphrases
breadth-first heuristic search memory requirement solution reconstruction best-first search strategy computational result domain-independent planning problem approximate breadth-first heuristic search algorithm bestfirst heuristic search divide-and-conquer solution reconstruction divide-and-conquer method approximate heuristic search algorithm recent work
