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Compact Translations of Non-disjunctive Answer Set Programs to Propositional Clauses

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Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6565))

Abstract

Propositional satisfiability (SAT) solvers provide a promising computational platform for logic programs under the stable model semantics. Computing stable models of a logic program using a SAT solver presumes translating the program into a set of clauses in the DIMACS format which is accepted by most SAT solvers as input. In this paper, we present succinct translations from programs with choice rules, cardinality rules, and weight rules—also known as smodels programs—to sets of clauses. These translations enable us to harness SAT solvers as black boxes to the task of computing stable models for logic programs generated by any smodels compatible grounder such as lparse or gringo. In the experimental part of this paper, we evaluate the potential of SAT solver technology in finding stable models using NP-complete benchmark problems employed in the Second Answer Set Programming Competition.

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References

  1. Apt, K., Blair, H., Walker, A.: Towards a theory of declarative knowledge. In: Foundations of Deductive Databases and Logic Programming, pp. 89–148. Morgan Kaufmann, San Francisco (1988)

    Chapter  Google Scholar 

  2. Ben-Eliyahu, R., Dechter, R.: Propositional Semantics for Disjunctive Logic Programs. Annals of Mathematics and Artificial Intelligence 12(1-2), 53–87 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  3. Clark, K.: Negation as failure. In: Logic and Data Bases, pp. 293–322. Plenum Press, New York (1978)

    Chapter  Google Scholar 

  4. Denecker, M., Vennekens, J., Bond, S., Gebser, M., Truszczyński, M.: The second answer set programming competition. In: Erdem, E., Lin, F., Schaub, T. (eds.) LPNMR 2009. LNCS, vol. 5753, pp. 637–654. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  5. Eén, N., Sörensson, N.: Translating pseudo-Boolean constraints into SAT. Journal on Satisfiability, Boolean Modeling and Computation 2(1-4), 1–26 (2006)

    MATH  Google Scholar 

  6. Erdem, E., Lifschitz, V.: Tight logic programs. Theory and Practice of Logic Programming 3(4-5), 499–518 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  7. Ferraris, P., Lifschitz, V.: Weight constraints as nested expressions. Theory and Practice of Logic Programming 5(1-2), 45–74 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  8. Gebser, M., Kaufmann, B., Schaub, T.: The conflict-driven answer set solver clasp: Progress report. In: Erdem, E., Lin, F., Schaub, T. (eds.) LPNMR 2009. LNCS, vol. 5753, pp. 509–514. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  9. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Proceedings of ICLP 1988, pp. 1070–1080 (1988)

    Google Scholar 

  10. Giunchiglia, E., Lierler, Y., Maratea, M.: Answer set programming based on propositional satisfiability. Journal of Automated Reasoning 36(4), 345–377 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  11. Janhunen, T.: Representing normal programs with clauses. In: Proceedings of ECAI 2004, pp. 358–362 (2004)

    Google Scholar 

  12. Janhunen, T.: Some (in)translatability results for normal logic programs and propositional theories. Journal of Applied Non-Classical Logics 16(1-2), 35–86 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  13. Janhunen, T., Niemelä, I., Sevalnev, M.: Computing stable models via reductions to difference logic. In: Erdem, E., Lin, F., Schaub, T. (eds.) LPNMR 2009. LNCS, vol. 5753, pp. 142–154. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Scarcello, F.: The DLV System for Knowledge Representation and Reasoning. ACM Transactions on Computational Logic 7(3), 499–562 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  15. Lierler, Y.: Cmodels – SAT-based disjunctive answer set solver. In: Baral, C., Greco, G., Leone, N., Terracina, G. (eds.) LPNMR 2005. LNCS (LNAI), vol. 3662, pp. 447–451. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  16. Lifschitz, V.: Answer set planning. In: Proceedings of ICLP 1999, pp. 23–37 (1999)

    Google Scholar 

  17. Lifschitz, V., Tang, L.R., Turner, H.: Nested expressions in logic programs. Annals of Mathematics and Artificial Intelligence 25(3-4), 369–389 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  18. Lin, F., Zhao, J.: On tight logic programs and yet another translation from normal logic programs to propositional logic. In: Proceedings of IJCAI 2003, pp. 853–858 (2003)

    Google Scholar 

  19. Lin, F., Zhao, Y.: ASSAT: computing answer sets of a logic program by SAT solvers. Artificial Intelligence 157(1-2), 115–137 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  20. Marek, V., Truszczyński, M.: Stable models and an alternative logic programming paradigm. In: The Logic Programming Paradigm: a 25-Year Perspective, pp. 375–398. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  21. Marek, V.W., Subrahmanian, V.S.: The relationship between stable, supported, default and autoepistemic semantics for general logic programs. Theoretical Computer Science 103, 365–386 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  22. Niemelä, I.: Logic programming with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25(3-4), 241–273 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  23. Niemelä, I.: Stable models and difference logic. Annals of Mathematics and Artificial Intelligence 53(1-4), 313–329 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  24. Simons, P., Niemelä, I., Soininen, T.: Extending and implementing the stable model semantics. Artificial Intelligence 138(1-2), 181–234 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  25. Tseitin, G.S.: On the complexity of derivation in propositional calculus. In: Siekmann, J., Wrightson, G. (eds.) Automation of Reasoning 2: Classical Papers on Computational Logic 1967–1970, pp. 466–483. Springer, Heidelberg (1983)

    Chapter  Google Scholar 

  26. Van Gelder, A., Ross, K.A., Schlipf, J.S.: The well-founded semantics for general logic programs. Journal of the ACM 38(3), 620–650 (1991)

    MathSciNet  MATH  Google Scholar 

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Janhunen, T., Niemelä, I. (2011). Compact Translations of Non-disjunctive Answer Set Programs to Propositional Clauses. In: Balduccini, M., Son, T.C. (eds) Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning. Lecture Notes in Computer Science(), vol 6565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20832-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-20832-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20831-7

  • Online ISBN: 978-3-642-20832-4

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