{"title":"通过计算约束图之间的时间接近指数来识别场景","authors":"Nicolas Ramaux, D. Fontaine","doi":"10.1109/TAI.1996.560788","DOIUrl":null,"url":null,"abstract":"The recognition of temporal scenarios is expressed by means of the proximity between a scenario (which represents a system's possible behavior) and a session (which describes the observed behaviour). This recognition is qualified with a proximity index, which allows one to classify, either online or offline, the scenario candidates for an explanation of the evolution. This approach, when used in a dynamic system's supervisory or diagnostic tasks, opens up possibilities for using or learning scenarios, or even for structuring the scenarios.","PeriodicalId":209171,"journal":{"name":"Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Recognising a scenario by calculating a temporal proximity index between constraint graphs\",\"authors\":\"Nicolas Ramaux, D. Fontaine\",\"doi\":\"10.1109/TAI.1996.560788\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The recognition of temporal scenarios is expressed by means of the proximity between a scenario (which represents a system's possible behavior) and a session (which describes the observed behaviour). This recognition is qualified with a proximity index, which allows one to classify, either online or offline, the scenario candidates for an explanation of the evolution. This approach, when used in a dynamic system's supervisory or diagnostic tasks, opens up possibilities for using or learning scenarios, or even for structuring the scenarios.\",\"PeriodicalId\":209171,\"journal\":{\"name\":\"Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAI.1996.560788\",\"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 Eighth IEEE International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1996.560788","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recognising a scenario by calculating a temporal proximity index between constraint graphs
The recognition of temporal scenarios is expressed by means of the proximity between a scenario (which represents a system's possible behavior) and a session (which describes the observed behaviour). This recognition is qualified with a proximity index, which allows one to classify, either online or offline, the scenario candidates for an explanation of the evolution. This approach, when used in a dynamic system's supervisory or diagnostic tasks, opens up possibilities for using or learning scenarios, or even for structuring the scenarios.
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