{"title":"用强连接树评价定性可能性影响图","authors":"Fatma Essghaier, N. Ben Amor, H. Fargier","doi":"10.1109/ICMSAO.2013.6552645","DOIUrl":null,"url":null,"abstract":"Possibilistic influence diagrams are decision graphical models in the possibilistic framework [1]. They present an alliance between decision theory, graph theory and possibilistic theory, in order to represent decision problems and define their optimal strategy through evaluation algorithms. In this paper we present a new approach to evaluate qualitative influence diagrams.","PeriodicalId":339666,"journal":{"name":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","volume":"253 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Evaluation of qualitative possibilistic influence diagrams using strong junction trees\",\"authors\":\"Fatma Essghaier, N. Ben Amor, H. Fargier\",\"doi\":\"10.1109/ICMSAO.2013.6552645\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Possibilistic influence diagrams are decision graphical models in the possibilistic framework [1]. They present an alliance between decision theory, graph theory and possibilistic theory, in order to represent decision problems and define their optimal strategy through evaluation algorithms. In this paper we present a new approach to evaluate qualitative influence diagrams.\",\"PeriodicalId\":339666,\"journal\":{\"name\":\"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)\",\"volume\":\"253 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMSAO.2013.6552645\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMSAO.2013.6552645","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evaluation of qualitative possibilistic influence diagrams using strong junction trees
Possibilistic influence diagrams are decision graphical models in the possibilistic framework [1]. They present an alliance between decision theory, graph theory and possibilistic theory, in order to represent decision problems and define their optimal strategy through evaluation algorithms. In this paper we present a new approach to evaluate qualitative influence diagrams.