{"title":"格式嵌套程序","authors":"Annamaria Bria, Wolfgang Faber, N. Leone","doi":"10.3233/FI-2009-179","DOIUrl":null,"url":null,"abstract":"Disjunctive logic programming under the answer set semantics (DLP, ASP) has been acknowledged as a versatile formalism for knowledge representation and reasoning during the last decade. Lifschitz, Tang, and Turner have introduced an extended language of DLP, called Nested Logic Programming (NLP), in 1999 [1]. It often allows for more concise representations by permitting a richer syntax in rule heads and bodies. However, that language is propositional and thus does not allow for variables, one of the strengths of DLP. \n \nIn this paper, we introduce a language similar to NLP, called Normal Form Nested (NPN) programs, which does allow for variables, and present the syntax and semantics. With the presence of variables, domain independence is no longer guaranteed. We study this issue in depth and define the class of safe NPNprograms, which are guaranteed to be domain independent. Moreover, we show that for NPNprograms which are also NLPs, our semantics coincides with the one of [1]; while keeping the standard meaning of answer sets on DLP programs with variables. Finally, we provide an algorithm which translates NPNprograms into DLPprograms, and does so in an efficient way, allowing for the effective implementation of the NPNlanguage on top of existing DLP systems.","PeriodicalId":56310,"journal":{"name":"Fundamenta Informaticae","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2008-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Normal Form Nested Programs\",\"authors\":\"Annamaria Bria, Wolfgang Faber, N. Leone\",\"doi\":\"10.3233/FI-2009-179\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Disjunctive logic programming under the answer set semantics (DLP, ASP) has been acknowledged as a versatile formalism for knowledge representation and reasoning during the last decade. Lifschitz, Tang, and Turner have introduced an extended language of DLP, called Nested Logic Programming (NLP), in 1999 [1]. It often allows for more concise representations by permitting a richer syntax in rule heads and bodies. However, that language is propositional and thus does not allow for variables, one of the strengths of DLP. \\n \\nIn this paper, we introduce a language similar to NLP, called Normal Form Nested (NPN) programs, which does allow for variables, and present the syntax and semantics. With the presence of variables, domain independence is no longer guaranteed. We study this issue in depth and define the class of safe NPNprograms, which are guaranteed to be domain independent. Moreover, we show that for NPNprograms which are also NLPs, our semantics coincides with the one of [1]; while keeping the standard meaning of answer sets on DLP programs with variables. Finally, we provide an algorithm which translates NPNprograms into DLPprograms, and does so in an efficient way, allowing for the effective implementation of the NPNlanguage on top of existing DLP systems.\",\"PeriodicalId\":56310,\"journal\":{\"name\":\"Fundamenta Informaticae\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2008-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fundamenta Informaticae\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.3233/FI-2009-179\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fundamenta Informaticae","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.3233/FI-2009-179","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
Disjunctive logic programming under the answer set semantics (DLP, ASP) has been acknowledged as a versatile formalism for knowledge representation and reasoning during the last decade. Lifschitz, Tang, and Turner have introduced an extended language of DLP, called Nested Logic Programming (NLP), in 1999 [1]. It often allows for more concise representations by permitting a richer syntax in rule heads and bodies. However, that language is propositional and thus does not allow for variables, one of the strengths of DLP.
In this paper, we introduce a language similar to NLP, called Normal Form Nested (NPN) programs, which does allow for variables, and present the syntax and semantics. With the presence of variables, domain independence is no longer guaranteed. We study this issue in depth and define the class of safe NPNprograms, which are guaranteed to be domain independent. Moreover, we show that for NPNprograms which are also NLPs, our semantics coincides with the one of [1]; while keeping the standard meaning of answer sets on DLP programs with variables. Finally, we provide an algorithm which translates NPNprograms into DLPprograms, and does so in an efficient way, allowing for the effective implementation of the NPNlanguage on top of existing DLP systems.
期刊介绍:
Fundamenta Informaticae is an international journal publishing original research results in all areas of theoretical computer science. Papers are encouraged contributing:
solutions by mathematical methods of problems emerging in computer science
solutions of mathematical problems inspired by computer science.
Topics of interest include (but are not restricted to):
theory of computing,
complexity theory,
algorithms and data structures,
computational aspects of combinatorics and graph theory,
programming language theory,
theoretical aspects of programming languages,
computer-aided verification,
computer science logic,
database theory,
logic programming,
automated deduction,
formal languages and automata theory,
concurrency and distributed computing,
cryptography and security,
theoretical issues in artificial intelligence,
machine learning,
pattern recognition,
algorithmic game theory,
bioinformatics and computational biology,
quantum computing,
probabilistic methods,
algebraic and categorical methods.