{"title":"智能家居的概率推理框架","authors":"Todor Dimitrov, J. Pauli, E. Naroska","doi":"10.1145/1376866.1376867","DOIUrl":null,"url":null,"abstract":"Inference and reasoning in modern AmI (Ambient Intelligence) middlewares is still a complex task. Currently no common patterns for building smart applications can be identified. This paper presents an ongoing effort to build a generic probabilistic reasoning framework for the networked homes. The framework can be utilized for designing smart agents in a systematic and unified way. The developed modeling and reasoning algorithms make an extensive use of the information about the user and the way he/she interacts with the system. To achieve this, several levels of knowledge representation are combined. Each level enriches the domain knowledge in a way that a consistent, user-adaptable probabilistic knowledge base is constructed. The facts in the knowledge base can be used to encode the logic for a specific application scenario.","PeriodicalId":364168,"journal":{"name":"workshop on Middleware for Pervasive and Ad-hoc Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2007-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A probabilistic reasoning framework for smart homes\",\"authors\":\"Todor Dimitrov, J. Pauli, E. Naroska\",\"doi\":\"10.1145/1376866.1376867\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Inference and reasoning in modern AmI (Ambient Intelligence) middlewares is still a complex task. Currently no common patterns for building smart applications can be identified. This paper presents an ongoing effort to build a generic probabilistic reasoning framework for the networked homes. The framework can be utilized for designing smart agents in a systematic and unified way. The developed modeling and reasoning algorithms make an extensive use of the information about the user and the way he/she interacts with the system. To achieve this, several levels of knowledge representation are combined. Each level enriches the domain knowledge in a way that a consistent, user-adaptable probabilistic knowledge base is constructed. The facts in the knowledge base can be used to encode the logic for a specific application scenario.\",\"PeriodicalId\":364168,\"journal\":{\"name\":\"workshop on Middleware for Pervasive and Ad-hoc Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"workshop on Middleware for Pervasive and Ad-hoc Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1376866.1376867\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"workshop on Middleware for Pervasive and Ad-hoc Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1376866.1376867","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A probabilistic reasoning framework for smart homes
Inference and reasoning in modern AmI (Ambient Intelligence) middlewares is still a complex task. Currently no common patterns for building smart applications can be identified. This paper presents an ongoing effort to build a generic probabilistic reasoning framework for the networked homes. The framework can be utilized for designing smart agents in a systematic and unified way. The developed modeling and reasoning algorithms make an extensive use of the information about the user and the way he/she interacts with the system. To achieve this, several levels of knowledge representation are combined. Each level enriches the domain knowledge in a way that a consistent, user-adaptable probabilistic knowledge base is constructed. The facts in the knowledge base can be used to encode the logic for a specific application scenario.