{"title":"SIGART(教程会话)","authors":"I. Goldstein, G. Hendrix, R. Fikes","doi":"10.1145/800191.805528","DOIUrl":null,"url":null,"abstract":"In the effort to construct intelligent computer systems, a primary consideration is how to represent large amounts of knowledge in a fashion that permits their effective use and interaction. Indeed, many researchers in the field of artificial intelligence have come to believe that knowledge representation is the fundamental issue in the attempt to understand intelligence. The presentations in this session explore this issue and describe two current important knowledge representation methodologies: frames and semantic nets.","PeriodicalId":379505,"journal":{"name":"ACM '76","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1976-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SIGART(Tutorial Session)\",\"authors\":\"I. Goldstein, G. Hendrix, R. Fikes\",\"doi\":\"10.1145/800191.805528\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the effort to construct intelligent computer systems, a primary consideration is how to represent large amounts of knowledge in a fashion that permits their effective use and interaction. Indeed, many researchers in the field of artificial intelligence have come to believe that knowledge representation is the fundamental issue in the attempt to understand intelligence. The presentations in this session explore this issue and describe two current important knowledge representation methodologies: frames and semantic nets.\",\"PeriodicalId\":379505,\"journal\":{\"name\":\"ACM '76\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1976-10-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM '76\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/800191.805528\",\"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 '76","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/800191.805528","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In the effort to construct intelligent computer systems, a primary consideration is how to represent large amounts of knowledge in a fashion that permits their effective use and interaction. Indeed, many researchers in the field of artificial intelligence have come to believe that knowledge representation is the fundamental issue in the attempt to understand intelligence. The presentations in this session explore this issue and describe two current important knowledge representation methodologies: frames and semantic nets.