Parallel Greedy Best-First Search with a Bound on Expansions Relative to Sequential Search
Abstract
Parallelization of non-admissible search algorithms such as GBFS poses a challenge because straightforward parallelization can result in search behavior which significantly deviates from sequential search. Previous work proposed PUHF, a parallel search algorithm which is constrained to only expand states that can be expanded by some tie-breaking strategy for GBFS. We show that despite this constraint, the number of states expanded by PUHF is not bounded by a constant multiple of the number of states expanded by sequential GBFS with the worst-case tie-breaking strategy. We propose and experimentally evaluate One Bench At a Time (OBAT), a parallel greedy search which guarantees that the number of states expanded is within a constant factor of the number of states expanded by sequential GBFS with some tie-breaking policy.
Keywords
Cite
@article{arxiv.2412.12221,
title = {Parallel Greedy Best-First Search with a Bound on Expansions Relative to Sequential Search},
author = {Takumi Shimoda and Alex Fukunaga},
journal= {arXiv preprint arXiv:2412.12221},
year = {2025}
}