| 研究課題/領域番号 |
20K11932
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| 研究機関 | 東京大学 |
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研究代表者 |
福永 ALEX 東京大学, 大学院総合文化研究科, 教授 (90452002)
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| 研究期間 (年度) |
2020-04-01 – 2024-03-31
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| キーワード | 探索 / 人工知能 / 並列アルゴリズム |
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| 研究実績の概要 |
In 2022-2023, we developed improved algorithms for parallel search. While parallelization of the A* graph search algorithm is fairly well-understood, parallelization of non-optimal best-first search algorithms such as Greedy Best-First Search (GBFS) has been much less understood. Recent work has proposed PUHF, a parallel GBFS which restricts 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, PUHF causes threads to spend much of the time waiting so that only states which are guaranteed to be in the BTS are expanded. We developed PUHF2, PUHF3, and PUHF4, three improvements to PUHF which maintain the constraint that only nodes in the BTS are epanded, but significantly reduce idle time and allow more rapid exploration of the BTS, resulting in better search performance compared to PUHF.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The goals of this project were (1) to analyze previously proposed parallel greedy best first search in order to understand how the behavior of parallel GBFS diverged from sequential GBFS, and (2) apply the theoretical insights obtained from (1) in order to develop new parallel GBFS algorithms which outperformed previous parallel GBFS strategies.
With regards to goal (1), our results published in (Kuroiwa and Fukunaga, 2020) showed that the behavior of previous parallel GBFS algorithms could diverge arbitrarily from sequential GBFS. More specifically, previous parallel GBFS algorithms could not be guaranteed to search no more than K times the nodes searched by sequential GBFS (for some constant K). Furthermore, it was shown that previous parallel GBFS algorithms expanded nodes which are not included the BTS, the set of expanded by sequential GBFS algorithms under some tie-breaking strategy.
Regarding goal (2), we proposed PUHF, a new parallel GBFS which is guaranteed to only expand nodes in the BTS (Kuroiwa and Fukunaga 2020).
Furthermore, in 2022-2023, we developed improvements to PUHF which significantly improved upon the performance of PUHF.
Thus, we believe the project is achieving the goals set forth in the project proposal.
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| 今後の研究の推進方策 |
In 2023-2024, we will continue to develop improved parallel Greedy Best-First Search algorithms. We plan to continue developing and evaluating improvements to PUHF. We will focus on methods which seek to reduce the amount of idle waiting incurred when threads must wait for a node which is guaranteed to be in the BTS.
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| 次年度使用額が生じた理由 |
Due to the COVID-19 pandemic, many international conferences relevent to this project were held online and not in person. As a result, conference travel expenses were significantly less than originally planned. As of 2023, many conferences have now resumed in-person attendance, so we plan to use the carryover funds for international travel in 2023.
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