The World of Combinatorial Fuzzy Problems and the Efficiency of Fuzzy Approximation Algorithms
University of Fukui（福井大学）
Tomoyuki Yamakami
备注：
A4, 10pt, 10 pages. This extended abstract already appeared in the Proceedings of the Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS 2014) and 15th International Symposium on Advanced Intelligent Systems (ISIS 2014), December 3-6, 2014, Institute of Electrical and Electronics Engineers (IEEE), pp. 29-35, 2014
We re-examine a practical aspect of combinatorial fuzzy problems of various types, including search, counting, optimization, and decision problems. We are focused only on those fuzzy problems that take series of fuzzy input objects and produce fuzzy values. To solve such problems efficiently, we design fast fuzzy algorithms, which are modeled by polynomial-time deterministic fuzzy Turing machines equipped with read-only auxiliary tapes and write-only output tapes and also modeled by polynomial-size fuzzy circuits composed of fuzzy gates. We also introduce fuzzy proof verification systems to model the fuzzification of nondeterminism. Those models help us identify four complexity classes: Fuzzy-FPA of fuzzy functions, Fuzzy-PA and Fuzzy-NPA of fuzzy decision problems, and Fuzzy-NPAO of fuzzy optimization problems. Based on a relative approximation scheme targeting fuzzy membership degree, we formulate two notions of "reducibility" in order to compare the computational complexity of two fuzzy problems. These reducibility notions make it possible to locate the most difficult fuzzy problems in Fuzzy-NPA and in Fuzzy-NPAO.
FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities
College of Cyberspace Security, Beijing University of Posts（网络安全学院，北京邮电大学）；Collaborative Innovation Center for Western Ecological Safety, Lanzhou University（西部生态安全协同创新中心，兰州大学）
Hierarchical Decision-Making for Autonomous Navigation: Integrating Deep Reinforcement Learning and Fuzzy Logic in Four-Wheel Independent Steering and Driving Systems
School of Automation, Central South University（自动化学院，中南大学）
Binarizing Physics-Inspired GNNs for Combinatorial Optimization
Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University（计算机科学系，电气工程学院，捷克技术大学）；Quantum Technologies Group, HSBC Labs, Singapore（量子技术组，汇丰实验室，新加坡）；Archimedes Research Unit on AI, Data Science and Algorithms, Athena Research Center（人工智能、数据科学与算法研究单元，阿提卡研究中心）
