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Effectively solving NP-SPEC encodings by translation to ASP.

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Title: Effectively solving NP-SPEC encodings by translation to ASP.
Authors: Alviano, Mario1 (AUTHOR) mario@alviano.net, Faber, Wolfgang1,2 (AUTHOR)
Source: Journal of Experimental & Theoretical Artificial Intelligence. Oct2015, Vol. 27 Issue 5, p577-601. 25p.
Subjects: Programming language semantics, Datalog (Computer program language), Boolean algebra, Knowledge representation (Information theory), Declarative programming
Abstract: NP-SPEC is a language for specifying problems in NP in a declarative way. Despite the fact that the semantics of the language was given by referring to Datalog with circumscription, which is very close to answer set programming (ASP), so far the only existing implementations are by means ofProlog and via Boolean satisfiability solvers. In this paper, we present translations from NP-SPEC to ASP, and provide an experimental evaluation of existing implementations and the proposed translations into ASP using various ASP solvers. The results show that translating into ASP clearly has an edge over the existing translation into SAT, which involves an intrinsic grounding process. We also argue that it might be useful to incorporate certain language constructs of NP-SPEC into mainstream ASP. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Effectively solving NP-SPEC encodings by translation to ASP.
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  Data: <searchLink fieldCode="DE" term="%22Programming+language+semantics%22">Programming language semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Datalog+%28Computer+program+language%29%22">Datalog (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Boolean+algebra%22">Boolean algebra</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+representation+%28Information+theory%29%22">Knowledge representation (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Declarative+programming%22">Declarative programming</searchLink>
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  Data: NP-SPEC is a language for specifying problems in NP in a declarative way. Despite the fact that the semantics of the language was given by referring to Datalog with circumscription, which is very close to answer set programming (ASP), so far the only existing implementations are by means ofProlog and via Boolean satisfiability solvers. In this paper, we present translations from NP-SPEC to ASP, and provide an experimental evaluation of existing implementations and the proposed translations into ASP using various ASP solvers. The results show that translating into ASP clearly has an edge over the existing translation into SAT, which involves an intrinsic grounding process. We also argue that it might be useful to incorporate certain language constructs of NP-SPEC into mainstream ASP. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/0952813X.2014.993505
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      – SubjectFull: Knowledge representation (Information theory)
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