{"title":"基于交互式POMDP的交互式服务组合模型","authors":"Jinyuan He, Le Sun","doi":"10.1109/IC3.2018.00030","DOIUrl":null,"url":null,"abstract":"Sequential decision making techniques have been applied to solve problems of Cloud service selection and composition. However, existing researches rarely focus on studying Cloud service compositions in a partially observable multiagent interactive environment. In this paper, we propose an interactive service composition model I-SerCom that applies the Interactive Partially Observable Markov Decision Process (I-POMDP) to help service buyers make sequential service selection policies. I-SerCom enables buyers make decisions by considering both changes of states of service marketplaces and an evolution of the intention of other involved agents. We use Interactive Influence Diagram (I-DID) to graphically represent the model. The I-DID factorization makes the model description more explicit and simplifies the model solving process. We use a case study of cloud service selection to present the process of I-SerCom, and conclude that I-SerCom is an efficient method for service composition.","PeriodicalId":236366,"journal":{"name":"2018 1st International Cognitive Cities Conference (IC3)","volume":"370 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Interactive Service Composition Model Based on Interactive POMDP\",\"authors\":\"Jinyuan He, Le Sun\",\"doi\":\"10.1109/IC3.2018.00030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sequential decision making techniques have been applied to solve problems of Cloud service selection and composition. However, existing researches rarely focus on studying Cloud service compositions in a partially observable multiagent interactive environment. In this paper, we propose an interactive service composition model I-SerCom that applies the Interactive Partially Observable Markov Decision Process (I-POMDP) to help service buyers make sequential service selection policies. I-SerCom enables buyers make decisions by considering both changes of states of service marketplaces and an evolution of the intention of other involved agents. We use Interactive Influence Diagram (I-DID) to graphically represent the model. The I-DID factorization makes the model description more explicit and simplifies the model solving process. We use a case study of cloud service selection to present the process of I-SerCom, and conclude that I-SerCom is an efficient method for service composition.\",\"PeriodicalId\":236366,\"journal\":{\"name\":\"2018 1st International Cognitive Cities Conference (IC3)\",\"volume\":\"370 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 1st International Cognitive Cities Conference (IC3)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IC3.2018.00030\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 1st International Cognitive Cities Conference (IC3)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC3.2018.00030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Interactive Service Composition Model Based on Interactive POMDP
Sequential decision making techniques have been applied to solve problems of Cloud service selection and composition. However, existing researches rarely focus on studying Cloud service compositions in a partially observable multiagent interactive environment. In this paper, we propose an interactive service composition model I-SerCom that applies the Interactive Partially Observable Markov Decision Process (I-POMDP) to help service buyers make sequential service selection policies. I-SerCom enables buyers make decisions by considering both changes of states of service marketplaces and an evolution of the intention of other involved agents. We use Interactive Influence Diagram (I-DID) to graphically represent the model. The I-DID factorization makes the model description more explicit and simplifies the model solving process. We use a case study of cloud service selection to present the process of I-SerCom, and conclude that I-SerCom is an efficient method for service composition.