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Lloyd-Topor completion and general stable models

Published online by Cambridge University Press:  25 September 2013

VLADIMIR LIFSCHITZ
Affiliation:
Department of Computer Science, The University of Texas at Austin (e-mail: vl@cs.utexas.edu, fkyang@cs.utexas.edu)
FANGKAI YANG
Affiliation:
Department of Computer Science, The University of Texas at Austin (e-mail: vl@cs.utexas.edu, fkyang@cs.utexas.edu)

Abstract

We investigate the relationship between the generalization of program completion defined in 1984 by Lloyd and Topor and the generalization of the stable model semantics introduced recently by Ferraris et al. The main theorem can be used to characterize, in some cases, the general stable models of a logic program by a first-order formula. The proof uses Truszczynski's stable model semantics of infinitary propositional formulas.

Type
Regular Papers
Copyright
Copyright © 2013 [VLADIMIR LIFSCHITZ and FANGKAI YANG] 

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References

Bartholomew, M. and Lee, J. 2012. Stable models of formulas with intensional functions. In Proceedings of International Conference on Principles of Knowledge Representation and Reasoning (KR).Google Scholar
Erdem, E. and Lifschitz, V. 2003. Tight logic programs. Theory and Practice of Logic Programming 3, 499518.CrossRefGoogle Scholar
Fages, F. 1994. Consistency of Clark's completion and existence of stable models. Journal of Methods of Logic in Computer Science 1, 5160.Google Scholar
Ferraris, P., Lee, J. and Lifschitz, V. 2011. Stable models and circumscription. Artificial Intelligence 175, 236263.CrossRefGoogle Scholar
Lee, J. and Meng, Y. 2011. First-order stable model semantics and first-order loop formulas. Journal of Artificial Inteligence Research (JAIR) 42, 125180.Google Scholar
Lifschitz, V. 1996. Foundations of logic programming. In Principles of Knowledge Representation, Brewka, G., Ed. CSLI Publications, 69128.Google Scholar
Lifschitz, V., Pearce, D. and Valverde, A. 2001. Strongly equivalent logic programs. ACM Transactions on Computational Logic 2, 526541.CrossRefGoogle Scholar
Lifschitz, V., Pearce, D. and Valverde, A. 2007. A characterization of strong equivalence for logic programs with variables. In Procedings of International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR), 188–200.Google Scholar
Lin, F. and Zhao, J. 2003. On tight logic programs and yet another translation from normal logic programs to propositional logic. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 853–864.Google Scholar
Lin, F. and Zhao, Y. 2004. ASSAT: Computing answer sets of a logic program by SAT solvers. Artificial Intelligence 157, 115137.CrossRefGoogle Scholar
Lloyd, J. and Topor, R. 1984. Making Prolog more expressive. Journal of Logic Programming 1, 225240.CrossRefGoogle Scholar
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