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Parallel-Data-Based Social Evolution Modeling

2021-06-15分类号:C934

【作者】Weishan Zhang  Zhaoxiang Hou  Xiao Wang  Zhidong Xu  Xin Liu  Fei-Yue Wang  
【部门】China University of Petroleum  Qingdao Academy of Intelligent Industry  State Key Laboratory of Management and Control for Complex Systems   Institute of Automation   Chinese Academy of Sciences  Institute of National Security  National Defense University  
【摘要】Abnormal or drastic changes in the natural environment may lead to unexpected events, such as tsunamis and earthquakes, which are becoming a major threat to national economy. Currently, no effective assessment approach can deduce a situation and determine the optimal response strategy when a natural disaster occurs.In this study, we propose a social evolution modeling approach and construct a deduction model for self-playing,self-learning, and self-upgrading on the basis of the idea of parallel data and reinforcement learning. The proposed approach can evaluate the impact of an event, deduce the situation, and provide optimal strategies for decisionmaking. Taking the breakage of a submarine cable caused by earthquake as an example, we find that the proposed modeling approach can obtain a higher reward compared with other existing methods.
【关键词】paral el data  reinforcement learning  decision-making
【基金】supported by the National Natural Science Foundation of China (No. 62072469);; the National Key R&D Program of China (No. 2018YFE0116700);; the Shandong Provincial Natural Science Foundation (No. ZR2019MF049, Parallel Data Driven Fault Prediction under On
【所属期刊栏目】Tsinghua Science and Technology
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