{"title":"逻辑与学习:从亚里士多德到神经网络","authors":"Vaishak Belle","doi":"10.3233/faia210350","DOIUrl":null,"url":null,"abstract":"The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence. In this chapter, we survey work that provides evidence for the long-standing and deep connections between logic and learning. After a brief historical prelude, our narrative is then structured in terms of three strands of interaction: logic versus learning, machine learning for logic, and logic for machine learning, but with ample overlap.","PeriodicalId":250200,"journal":{"name":"Neuro-Symbolic Artificial Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Logic Meets Learning: From Aristotle to Neural Networks\",\"authors\":\"Vaishak Belle\",\"doi\":\"10.3233/faia210350\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence. In this chapter, we survey work that provides evidence for the long-standing and deep connections between logic and learning. After a brief historical prelude, our narrative is then structured in terms of three strands of interaction: logic versus learning, machine learning for logic, and logic for machine learning, but with ample overlap.\",\"PeriodicalId\":250200,\"journal\":{\"name\":\"Neuro-Symbolic Artificial Intelligence\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuro-Symbolic Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/faia210350\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuro-Symbolic Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/faia210350","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Logic Meets Learning: From Aristotle to Neural Networks
The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence. In this chapter, we survey work that provides evidence for the long-standing and deep connections between logic and learning. After a brief historical prelude, our narrative is then structured in terms of three strands of interaction: logic versus learning, machine learning for logic, and logic for machine learning, but with ample overlap.