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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 decision-making. 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.


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Parallel-Data-Based Social Evolution Modeling

Show Author's information Weishan Zhang( )Zhaoxiang HouXiao WangZhidong XuXin LiuFei-Yue Wang
China University of Petroleum, Qingdao 266580, China
Qingdao Academy of Intelligent Industry, Qingdao 266111, China
China University of Petroleum, Qingdao 266580, China
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China
Qingdao Academy of Intelligent Industry, Qingdao 266111, China
Institute of National Security, National Defense University, Beijing 100081, China

Abstract

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 decision-making. 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.

Keywords: reinforcement learning, parallel data, decision-making

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Publication history

Received: 20 September 2020
Accepted: 09 October 2020
Published: 09 June 2021
Issue date: December 2021

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© The author(s) 2021.

Acknowledgements

This research was 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 Online-Offline Combined Cloud Computing Environment), and the Fundamental Research Funds for the Central Universities (No. 2015020031).

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