Integrating answer set programming and constraint logic programming

Veena S. Mellarkod; Michael Gelfond; Yuanlin Zhang

doi:10.1007/s10472-009-9116-y

Integrating answer set programming and constraint logic programming

Veena S. Mellarkod, Michael Gelfond, Yuanlin Zhang

Computer Science

Research output: Contribution to journal › Article › peer-review

88 Scopus citations

Abstract

We introduce a knowledge representation language AC(C)extending the syntax and semantics of ASP and CR-Prolog, give some examples of its use, and present an algorithm, AC!solver, for computing answer sets of AC(C)programs. The algorithm does not require full grounding of a program and combines "classical" ASP solving methods with constraint logic programming techniques and CR-Prolog based abduction. The AC(C) based approach often allows to solve problems which are impossible to solve by more traditional ASP solving techniques. We believe that further investigation of the language and development of more efficient and reliable solvers for its programs can help to substantially expand the domain of applicability of the answer set programming paradigm.

Original language	English
Pages (from-to)	251-287
Number of pages	37
Journal	Annals of Mathematics and Artificial Intelligence
Volume	53
Issue number	1-4
DOIs	https://doi.org/10.1007/s10472-009-9116-y
State	Published - Aug 2008

Keywords

Answer set programming
Constraint logic programming
Knowledge representation and reasoning

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10.1007/s10472-009-9116-y

Cite this

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abstract = "We introduce a knowledge representation language AC(C)extending the syntax and semantics of ASP and CR-Prolog, give some examples of its use, and present an algorithm, AC!solver, for computing answer sets of AC(C)programs. The algorithm does not require full grounding of a program and combines {"}classical{"} ASP solving methods with constraint logic programming techniques and CR-Prolog based abduction. The AC(C) based approach often allows to solve problems which are impossible to solve by more traditional ASP solving techniques. We believe that further investigation of the language and development of more efficient and reliable solvers for its programs can help to substantially expand the domain of applicability of the answer set programming paradigm.",

keywords = "Answer set programming, Constraint logic programming, Knowledge representation and reasoning",

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note = "Funding Information: Acknowledgements This work was supported in part by NASA contract NASA-NEG05GP48G and ATEE/DTO contract ASU-06-C-0143. We thank Roland H. C. Yap for many discussions and help in our implementation of the prototype system ACengine.",

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AU - Gelfond, Michael

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N2 - We introduce a knowledge representation language AC(C)extending the syntax and semantics of ASP and CR-Prolog, give some examples of its use, and present an algorithm, AC!solver, for computing answer sets of AC(C)programs. The algorithm does not require full grounding of a program and combines "classical" ASP solving methods with constraint logic programming techniques and CR-Prolog based abduction. The AC(C) based approach often allows to solve problems which are impossible to solve by more traditional ASP solving techniques. We believe that further investigation of the language and development of more efficient and reliable solvers for its programs can help to substantially expand the domain of applicability of the answer set programming paradigm.

AB - We introduce a knowledge representation language AC(C)extending the syntax and semantics of ASP and CR-Prolog, give some examples of its use, and present an algorithm, AC!solver, for computing answer sets of AC(C)programs. The algorithm does not require full grounding of a program and combines "classical" ASP solving methods with constraint logic programming techniques and CR-Prolog based abduction. The AC(C) based approach often allows to solve problems which are impossible to solve by more traditional ASP solving techniques. We believe that further investigation of the language and development of more efficient and reliable solvers for its programs can help to substantially expand the domain of applicability of the answer set programming paradigm.

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Integrating answer set programming and constraint logic programming

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