{"title":"通过HDPTN呈现现实世界现象","authors":"A. Poggi, G. Adorni","doi":"10.1109/TAI.1991.167049","DOIUrl":null,"url":null,"abstract":"The authors present a model, called HDPTN (hierarchical and distributed process transition network), for the representation of real world phenomena. This model is based on two types of entities: circumstances and episodes. A circumstance is an elementary entity which can take on a set of discrete values over time, while an episode is a compound entity whose parts are a circumstance and/or other episodes acting concurrently. In particular, the hierarchical and distributed structure of the episode seems to be suitable for the modeling of systems which need real-time performance and the integration of representations and reasoning at different levels of detail.<<ETX>>","PeriodicalId":371778,"journal":{"name":"[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Representing real world phenomena through HDPTN\",\"authors\":\"A. Poggi, G. Adorni\",\"doi\":\"10.1109/TAI.1991.167049\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors present a model, called HDPTN (hierarchical and distributed process transition network), for the representation of real world phenomena. This model is based on two types of entities: circumstances and episodes. A circumstance is an elementary entity which can take on a set of discrete values over time, while an episode is a compound entity whose parts are a circumstance and/or other episodes acting concurrently. In particular, the hierarchical and distributed structure of the episode seems to be suitable for the modeling of systems which need real-time performance and the integration of representations and reasoning at different levels of detail.<<ETX>>\",\"PeriodicalId\":371778,\"journal\":{\"name\":\"[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAI.1991.167049\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1991.167049","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The authors present a model, called HDPTN (hierarchical and distributed process transition network), for the representation of real world phenomena. This model is based on two types of entities: circumstances and episodes. A circumstance is an elementary entity which can take on a set of discrete values over time, while an episode is a compound entity whose parts are a circumstance and/or other episodes acting concurrently. In particular, the hierarchical and distributed structure of the episode seems to be suitable for the modeling of systems which need real-time performance and the integration of representations and reasoning at different levels of detail.<>