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.
Cite
Text
Shimoda and Fukunaga. "Parallel Greedy Best-First Search with a Bound on Expansions Relative to Sequential Search." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I25.34869Markdown
[Shimoda and Fukunaga. "Parallel Greedy Best-First Search with a Bound on Expansions Relative to Sequential Search." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/shimoda2025aaai-parallel/) doi:10.1609/AAAI.V39I25.34869BibTeX
@inproceedings{shimoda2025aaai-parallel,
title = {{Parallel Greedy Best-First Search with a Bound on Expansions Relative to Sequential Search}},
author = {Shimoda, Takumi and Fukunaga, Alex},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {26668-26677},
doi = {10.1609/AAAI.V39I25.34869},
url = {https://mlanthology.org/aaai/2025/shimoda2025aaai-parallel/}
}