2020 Fiscal Year Research-status Report
| Project/Area Number |
20K11932
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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 – 2023-03-31
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| Keywords | 探索 / Heuristic Search |
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| Outline of Annual Research Achievements |
Recent work has experimentally shown that parallelization of Greedy Best-First Search (GBFS), a satisficing best-first search method, can behave very differently from sequential GBFS. In this paper, we propose a theoretical framework to compare parallel best-first search with sequential best-first search, including both suboptimal (GBFS, Weighted A*) and optimal (A*) best-first search methods. We analyze the extent to which the search behavior of existing parallel best-first search methods differ from sequential best-first search, and show that existing methods are vulnerable to pathological behavior, and that they can expand nodes which would not be expanded by sequential search under any tie-breaking policy. We also propose PUHF, a parallel best-first search which is guaranteed to expand a node only if there is some tie-breaking strategy for sequential search which expands the node.
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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
In the proposal, we planned to perform a theoretical analysis of parallel best first search in the first year. We successfully analyzed parallel best-first search, showing that there exist search spaces where the search behavior of existing parallel best first search can be arbitrarily worse than sequential best first search. We also proposed a new variant of parallel greedy best first search which offers some theoretical guarantees on the degradation relative to sequential search.
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| Strategy for Future Research Activity |
We plan to continue the theoretical analysis and improvement of parallel best first search, focusing on developing parallel algorithms which have bounded performance guarantees relative to sequential search.
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| Causes of Carryover |
Although we made substantial progress on the research, spending of the research funds for this project was delayed due to the pandemic, as international conference travel was not possible. The amount carried over will be spent in the next year.
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Research Products
(1 results)
