{"title":"DELTA:解决柴油电力机车故障的专家系统","authors":"P. Bonissone","doi":"10.1145/800173.809697","DOIUrl":null,"url":null,"abstract":"In the last few years, expert systems have become the most visible and fastest growing branch of Artificial Intelligence. Their objective is to capture the knowledge of an expert in a particular problem domain, represent it in a modular, expandable structure, and transfer it to other users in the same problem domain. To accomplish this goal, it is necessary to address issues of knowledge acquisition, knowledge representation, inference mechanisms, control strategies, user interface and dealing with uncertainty.","PeriodicalId":306306,"journal":{"name":"ACM '83","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"DELTA: An expert system to troubleshoot diesel electric locomotives\",\"authors\":\"P. Bonissone\",\"doi\":\"10.1145/800173.809697\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the last few years, expert systems have become the most visible and fastest growing branch of Artificial Intelligence. Their objective is to capture the knowledge of an expert in a particular problem domain, represent it in a modular, expandable structure, and transfer it to other users in the same problem domain. To accomplish this goal, it is necessary to address issues of knowledge acquisition, knowledge representation, inference mechanisms, control strategies, user interface and dealing with uncertainty.\",\"PeriodicalId\":306306,\"journal\":{\"name\":\"ACM '83\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM '83\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/800173.809697\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM '83","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/800173.809697","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DELTA: An expert system to troubleshoot diesel electric locomotives
In the last few years, expert systems have become the most visible and fastest growing branch of Artificial Intelligence. Their objective is to capture the knowledge of an expert in a particular problem domain, represent it in a modular, expandable structure, and transfer it to other users in the same problem domain. To accomplish this goal, it is necessary to address issues of knowledge acquisition, knowledge representation, inference mechanisms, control strategies, user interface and dealing with uncertainty.