| 研究実績の概要 |
In 2018, the algorithms and applications underlying Genetic Counterfactual Programming (GCP) in computer-aided design and robotics were developed: (1) the new algorithms for computing succinct networks in polygonal maps, enabling the enhanced scalability and efficiency in cluttered environments; (2) the new algorithms for representing general combination objects succinctly, realizing the utmost efficiency in time and space complexity; (3) the applications of learning networks with variable size in the data-driven design of vehicle layouts, enabling the generation of new vehicle models outperforming the frontiers of mileage consumption; (4) the applications of newly developed log-aesthetic polynomial curves in path planning, realizing the fast computation of smoother/safer curves for transportation/navigation; (5) the learning networks for (prosthetic) hand robotic interfaces and human activity recognition, achieving the highest accuracy in challenging testing environments; (6) the algorithms for coordinated synchrony realizing the seamless generation of flexible control rules in complex and changing environments; (7) the new learning algorithms that evolve hierarchical networks to generate decentralized/distributed/non-overlapping modules, whose application is potential to tackle the design of complex graphs/networks with best quasi-linear time complexity; (8) the new optimization algorithms for computing the succinct representation of large/distributed graphs and networks, achieving the encoding with utmost performance efficiency (in some cases, using 1 bit).
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