{"title":"模糊定性行为优先级","authors":"G. Coghill","doi":"10.1109/FUZZY.2007.4295517","DOIUrl":null,"url":null,"abstract":"Fuzzy qualitative simulation combines the features of qualitative simulation and fuzzy reasoning in order to gain advantages from both. However, the output of a fuzzy qualitative simulation process is a behaviour tree which for complex systems will be large. In order to overcome this and permit focussing on preferred behaviours priortisation was developed. In this paper a new prioritisation scheme is presented that makes use of both constraint and temporal information to perform the prioritisation.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Fuzzy Qualitative Behaviour Prioritisation\",\"authors\":\"G. Coghill\",\"doi\":\"10.1109/FUZZY.2007.4295517\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fuzzy qualitative simulation combines the features of qualitative simulation and fuzzy reasoning in order to gain advantages from both. However, the output of a fuzzy qualitative simulation process is a behaviour tree which for complex systems will be large. In order to overcome this and permit focussing on preferred behaviours priortisation was developed. In this paper a new prioritisation scheme is presented that makes use of both constraint and temporal information to perform the prioritisation.\",\"PeriodicalId\":236515,\"journal\":{\"name\":\"2007 IEEE International Fuzzy Systems Conference\",\"volume\":\"73 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE International Fuzzy Systems Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZY.2007.4295517\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Fuzzy Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.2007.4295517","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy qualitative simulation combines the features of qualitative simulation and fuzzy reasoning in order to gain advantages from both. However, the output of a fuzzy qualitative simulation process is a behaviour tree which for complex systems will be large. In order to overcome this and permit focussing on preferred behaviours priortisation was developed. In this paper a new prioritisation scheme is presented that makes use of both constraint and temporal information to perform the prioritisation.
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