{"title":"基于强化学习的意见动态与共识达成策略","authors":"Mingwei Wang, Fangshun Li, Decui Liang","doi":"10.1109/ISSPIT51521.2020.9408808","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Opinion dynamics and consensus achievement strategy based on reinforcement learning\",\"authors\":\"Mingwei Wang, Fangshun Li, Decui Liang\",\"doi\":\"10.1109/ISSPIT51521.2020.9408808\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":111385,\"journal\":{\"name\":\"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPIT51521.2020.9408808\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPIT51521.2020.9408808","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Opinion dynamics and consensus achievement strategy based on reinforcement learning
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.