{"title":"基于agent偏好的群体角色分配","authors":"Haibin Zhu, Y. Zhu","doi":"10.1109/ICNSC.2017.8000160","DOIUrl":null,"url":null,"abstract":"Using the Role-Based Collaboration (RBC) methodology that advocates collectivism and the Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model, this paper formalizes the problem of considering individual preferences in a group with the support of Group Role Assignment (GRA); analyzes the impact of agents' preferences on group performance through simulations. The contributions of this work include: 1) formalization of the proposed problem, i.e. GRA with Agents' Preferences (GRAAP); 2) a practical solution to the problem, i.e., GRA with Integrated Agents' Preferences (GRAIAP); 3) benefit verification through simulations; and 4) a conclusion that can be applied to industries requiring collaborative effort.","PeriodicalId":145129,"journal":{"name":"2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)","volume":"200 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Group Role Assignment with Agents' Preferences\",\"authors\":\"Haibin Zhu, Y. Zhu\",\"doi\":\"10.1109/ICNSC.2017.8000160\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Using the Role-Based Collaboration (RBC) methodology that advocates collectivism and the Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model, this paper formalizes the problem of considering individual preferences in a group with the support of Group Role Assignment (GRA); analyzes the impact of agents' preferences on group performance through simulations. The contributions of this work include: 1) formalization of the proposed problem, i.e. GRA with Agents' Preferences (GRAAP); 2) a practical solution to the problem, i.e., GRA with Integrated Agents' Preferences (GRAIAP); 3) benefit verification through simulations; and 4) a conclusion that can be applied to industries requiring collaborative effort.\",\"PeriodicalId\":145129,\"journal\":{\"name\":\"2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)\",\"volume\":\"200 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNSC.2017.8000160\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNSC.2017.8000160","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using the Role-Based Collaboration (RBC) methodology that advocates collectivism and the Environments - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model, this paper formalizes the problem of considering individual preferences in a group with the support of Group Role Assignment (GRA); analyzes the impact of agents' preferences on group performance through simulations. The contributions of this work include: 1) formalization of the proposed problem, i.e. GRA with Agents' Preferences (GRAAP); 2) a practical solution to the problem, i.e., GRA with Integrated Agents' Preferences (GRAIAP); 3) benefit verification through simulations; and 4) a conclusion that can be applied to industries requiring collaborative effort.