| 研究実績の概要 |
The foundations for learning and optimization of graphs via enumerative queries and a their application benchmarks in computer-aided design and robotics were proposed: (1) the sampling and learning mechanism for obstacle-avoiding lattice paths the enumerative encoding and gradient-free heuristics, (2) the learning of adaptive locomotion gaits for six-legged robots under conditions of leg failure by using the enumerative queries, (3) the compact representation of convolution graphs by broadcast networks and its application to sound classification, (4) the representation of curvature in the folding of planar membranes, allowing the representation of folding structures by compact planar graphs, (5) the development of multi-modal path planning, allowing the efficient computation of collision-free paths for multi-agent systems in lattice paths, (6) the sampling of graph-based cable-driven robotic structures, enabling the design of cable-driven mechanical structures by enumerative sampling of the graph search space, (7) the study of fairness functionals for smooth path planning in mobile robots, allowing the efficient evaluation of smoothness over a network of multiple robotics trajectories, and the (8) applications of graph-based sampling and modeling of robotic structures, having the potential for enumerative and efficient queries of robotic topologies.
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| 今後の研究の推進方策 |
In 2022, the research on the enumerative representation of hypergraphs and the further applications of Learning Graphs with Enumerative Queries is to be conducted. The study of learning optimal Bayesian Network Structures by combinatorial decompositions is to be conducted. Also, the application in architecture design of Convolutional Neural Networks (tensor decompositions) and the planning problems in robotics are to be conducted.
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| 次年度使用額が生じた理由 |
The reasons for incurring the amount to be used in next fiscal year are as follows: (1) Regarding Article Costs, savings are due to the fact of postponing the acquisition of a computer for numerical computations to the next fiscal year. (2) Regarding Travel Expenses, the savings are due to attending virtual conferences (pandemic situation). (3) Regarding miscellaneous costs, the journal articles corresponding to the extensions / developments of the ideas presented at conferences in the fiscal year 2021 are under submission/preparation. The ammount to be used in the next fiscal year is to be split among the costs for acquiring a computing environment suitable for parallel numerical computations, and the costs for publishing at relevant conference and journals venues.
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