2023 Fiscal Year Final Research Report
Research on Learning Graphs via Enumerative Queries and its Applications
| Project/Area Number |
20K11998
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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 61040:Soft computing-related
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| Research Institution | Waseda University |
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Principal Investigator |
Parque Victor 早稲田大学, 理工学術院, 准教授(任期付) (50745221)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Keywords | graph learning / optimization / networks / design / optimization / planning / robotics |
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| Outline of Final Research Achievements |
In this research, learning graph structures via enumerative queries and their applications were conducted. In one hand, the efficient algorithms to design structures of directed networks, undirected networks, modular networks, and variable-size networks achieving the state-of-the-art efficiency were made possible. And the applications in path planning and graph design and optimization problems have shown the desirable performance, efficiency, and versatility of the proposed algorithms.
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| Free Research Field |
Computational Intelligence
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| Academic Significance and Societal Importance of the Research Achievements |
Learning graphs via enumerative queries bring the unique benefits of succinctness (theoretical bounds) and efficiency to sample richer knowledge structures (parallelization), rendering the improved approaches to tackle complex problems in graph design/optimization in robotics and engineering.
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