Functional Stable Model Semantics and Answer Set Programming Modulo Theories
This academic paper, submitted to arXiv by Michael Bartholomew and Joohyung Lee, explores the integration of intensional functions into Answer Set Programming (ASP). Intensional functions allow values to be described by other functions and predicates, offering greater flexibility than pre-defined standard ASP structures. The authors demonstrate that functional stable model semantics are crucial within the framework of Answer Set Programming Modulo Theories (ASPMT), which tightly integrates ASP with Satisfiability Modulo Theories (SMT). This approach generalizes existing integration methods, viewing them as special cases with limited functional roles. A key contribution is showing that tight ASPMT programs can be translated into SMT instances, mirroring the established relationship between ASP and SAT solvers. Although the arXiv submission date is listed as May 2026, the work was originally published in the Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI) in 2013. The research advances theoretical computer science by enhancing the expressive power of logic programming through semantic frameworks.
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Functional Stable Model Semantics and Answer Set Programming Modulo Theories
This academic paper, submitted to arXiv by Michael Bartholomew and Joohyung Lee, explores the integration of intensional functions into Answer Set Programming (ASP). Intensional functions allow values to be described by other functions and predicates, offering greater flexibility than pre-defined standard ASP structures. The authors demonstrate that functional stable model semantics are crucial within the framework of Answer Set Programming Modulo Theories (ASPMT), which tightly integrates ASP with Satisfiability Modulo Theories (SMT). This approach generalizes existing integration methods, viewing them as special cases with limited functional roles. A key contribution is showing that tight ASPMT programs can be translated into SMT instances, mirroring the established relationship between ASP and SAT solvers. Although the arXiv submission date is listed as May 2026, the work was originally published in the Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI) in 2013. The research advances theoretical computer science by enhancing the expressive power of logic programming through semantic frameworks.
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