{"title":"在实际因果关系分析中包含背景知识:一种答案集规划方法","authors":"MICHAEL GELFOND, JORGE FANDINNO, EVGENII BALAI","doi":"10.1017/s1471068423000248","DOIUrl":null,"url":null,"abstract":"Abstract This paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual causality. A definition of cause is presented and used to analyze the actual causes of changes with respect to sequences of actions representing those examples.","PeriodicalId":49436,"journal":{"name":"Theory and Practice of Logic Programming","volume":"26 1","pages":"0"},"PeriodicalIF":1.4000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach\",\"authors\":\"MICHAEL GELFOND, JORGE FANDINNO, EVGENII BALAI\",\"doi\":\"10.1017/s1471068423000248\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual causality. A definition of cause is presented and used to analyze the actual causes of changes with respect to sequences of actions representing those examples.\",\"PeriodicalId\":49436,\"journal\":{\"name\":\"Theory and Practice of Logic Programming\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Theory and Practice of Logic Programming\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/s1471068423000248\",\"RegionNum\":2,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Theory and Practice of Logic Programming","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/s1471068423000248","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach
Abstract This paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual causality. A definition of cause is presented and used to analyze the actual causes of changes with respect to sequences of actions representing those examples.
期刊介绍：
Theory and Practice of Logic Programming emphasises both the theory and practice of logic programming. Logic programming applies to all areas of artificial intelligence and computer science and is fundamental to them. Among the topics covered are AI applications that use logic programming, logic programming methodologies, specification, analysis and verification of systems, inductive logic programming, multi-relational data mining, natural language processing, knowledge representation, non-monotonic reasoning, semantic web reasoning, databases, implementations and architectures and constraint logic programming.
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