{"title":"预测专家代理方法","authors":"Afef Selmi, Zaki Brahmi, M. Gammoudi","doi":"10.1109/WETICE49692.2020.00012","DOIUrl":null,"url":null,"abstract":"In multi-agent recommender system, the knowledge degree of an agent and its trust degree are two main criteria in the decision-making phase. These criteria are used to recommend the expert agent. Therefore, how to model agent and evaluate its trust is becoming a challenging issue. This problem can affect the whole prediction of expert agents. In this paper, we propose a Predicting Expert Agents approach (PEA). We applied a clustering method, Fuzzy Formal Concepts Analysis, to model agent and evaluate its trust and a probabilistic method, Theory of Belief Functions, to predict the expert agent.","PeriodicalId":114214,"journal":{"name":"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PEA: Predicting Expert Agents approach\",\"authors\":\"Afef Selmi, Zaki Brahmi, M. Gammoudi\",\"doi\":\"10.1109/WETICE49692.2020.00012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In multi-agent recommender system, the knowledge degree of an agent and its trust degree are two main criteria in the decision-making phase. These criteria are used to recommend the expert agent. Therefore, how to model agent and evaluate its trust is becoming a challenging issue. This problem can affect the whole prediction of expert agents. In this paper, we propose a Predicting Expert Agents approach (PEA). We applied a clustering method, Fuzzy Formal Concepts Analysis, to model agent and evaluate its trust and a probabilistic method, Theory of Belief Functions, to predict the expert agent.\",\"PeriodicalId\":114214,\"journal\":{\"name\":\"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WETICE49692.2020.00012\",\"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 29th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WETICE49692.2020.00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
在多智能体推荐系统中，智能体的知识程度和信任程度是决策阶段的两个主要标准。这些标准用于推荐专家代理。因此，如何对智能体进行建模并评估其信任成为一个具有挑战性的问题。这个问题会影响专家代理的整个预测。本文提出了一种预测专家代理方法(PEA)。我们采用聚类方法——模糊形式概念分析(Fuzzy Formal Concepts Analysis)对智能体建模并评估其信任程度，采用概率方法——信念函数理论(Theory of Belief Functions)对专家智能体进行预测。
In multi-agent recommender system, the knowledge degree of an agent and its trust degree are two main criteria in the decision-making phase. These criteria are used to recommend the expert agent. Therefore, how to model agent and evaluate its trust is becoming a challenging issue. This problem can affect the whole prediction of expert agents. In this paper, we propose a Predicting Expert Agents approach (PEA). We applied a clustering method, Fuzzy Formal Concepts Analysis, to model agent and evaluate its trust and a probabilistic method, Theory of Belief Functions, to predict the expert agent.
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