Separate Generation and Evaluation for Parallel Greedy Best-First Search
<|reference_start|>Separate Generation and Evaluation for Parallel Greedy Best-First Search: Parallelization of Greedy Best First Search (GBFS) has been difficult because straightforward parallelization can result in search behavior which differs significantly from sequential GBFS, exploring states which would not be explored by sequential GBFS with any tie-breaking strategy. Recent work has proposed a class of parallel GBFS algorithms which constrains search to exploration of the Bench Transition System (BTS), which is the set of states that can be expanded by GBFS under some tie-breaking policy. However, enforcing this constraint is costly, as such BTS-constrained algorithms are forced to spend much of the time waiting so that only states which are guaranteed to be in the BTS are expanded. We propose an improvement to parallel search which decouples state generation and state evaluation and significantly improves state evaluation rate, resulting in better search performance.<|reference_end|>
@article{shimoda2024separate,
    title={Separate Generation and Evaluation for Parallel Greedy Best-First Search},
    author={Takumi Shimoda and Alex Fukunaga},
    journal={arXiv preprint arXiv:2408.05682},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05682},
    primaryClass={cs.AI cs.DC cs.DS}
}