| Project/Area Number |
20K11932
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
福永 ALEX 東京大学, 大学院総合文化研究科, 教授 (90452002)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Project Status |
Granted (Fiscal Year 2022)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2021: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2020: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | 探索 / 人工知能 / 並列アルゴリズム / Heuristic Search |
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| Outline of Research at the Start |
ロボット等のエージェントの自動行動計画等においてグラフ探索アルゴリズムが広く応用されている。大規模な問題を限られた時間内に解くには探索アルゴリズムの並列化が必要である。最短経路を求めるアルゴリズムについてはある程度効率良い並列化手法が提案されている。一方、大規模な問題の場合、最短経路を求めるのは困難な為、GBFS等、限られた時間内でなるべく良い経路を求めるアルゴリズムが使用されている。GBFSの効果的な並列化手法はアンサンブル法による以外、開発されていない。本研究ではGBFSの効率的な並列化手法の開発及び理論的解析を試みる。
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| Outline of Annual Research Achievements |
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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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
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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| Strategy for Future Research Activity |
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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Report
(3 results)
Research Products
(3 results)
