Opinion dynamics and consensus achievement strategy based on reinforcement learning

被引:2
作者
Wang, Mingwei [1 ]
Li, Fangshun [1 ]
Liang, Decui [1 ]
机构
[1] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 610054, Peoples R China
来源
2020 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT 2020) | 2020年
基金
中国国家自然科学基金;
关键词
Opinion management; Consensus boost; Opinion guidance; Opinion dynamics; Reinforcement learning; FUSION PROCESS;
D O I
10.1109/ISSPIT51521.2020.9408808
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Consensus boost and opinion guidance are two important problems during the opinion management process. Considering that opinion interaction with opinion dynamics, this paper formalizes the two problems as markov decision process. To solve the two problems with minimum cost, we proposes consensus boost algorithm and opinion guidance algorithm based on reinforcement learning. Meantime, we construct opinion management framework by combining consensus boost algorithm and opinion guidance algorithm which is beneficial to the opinion management of managers. Finally, through experimental analysis, we verify the effectiveness and properties of the proposed framework.
引用
收藏
页数:6
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