The Second Answer Set Programming Competition

  • Marc Denecker
  • Joost Vennekens
  • Stephen Bond
  • Martin Gebser
  • Mirosław Truszczyński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5753)

Abstract

This paper reports on the Second Answer Set Programming Competition. The competitions in areas of Satisfiability checking, Pseudo-Boolean constraint solving and Quantified Boolean Formula evaluation have proven to be a strong driving force for a community to develop better performing systems. Following this experience, the Answer Set Programming competition series was set up in 2007, and ran as part of the International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR). This second competition, held in conjunction with LPNMR 2009, differed from the first one in two important ways. First, while the original competition was restricted to systems designed for the answer set programming language, the sequel was open to systems designed for other modeling languages, as well. Consequently, among the contestants of the second competition were a CLP(FD) team and three model generation systems for (extensions of) classical logic. Second, this latest competition covered not only satisfiability problems but also optimization ones. We present and discuss the set-up and the results of the competition.

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References

  1. 1.
    Marek, V., Truszczyński, M.: Stable models and an alternative logic programming paradigm. In: Apt, K., Marek, W., Truszczyński, M., Warren, D. (eds.) The Logic Programming Paradigm: a 25-Year Perspective, pp. 375–398. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    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)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge (2003)CrossRefMATHGoogle Scholar
  4. 4.
    Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Generation Computing 9(3-4), 365–385 (1991)CrossRefMATHGoogle Scholar
  5. 5.
    Colmerauer, A., Kanoui, H., Pasero, R., Roussel, P.: Un systeme de communication homme-machine en Francais. Technical report, University of Marseille (1973)Google Scholar
  6. 6.
    Kowalski, R.: Predicate logic as a programming language. In: Rosenfeld, J. (ed.) Proceedings of the Congress of the International Federation for Information Processing, pp. 569–574. North Holland, Amsterdam (1974)Google Scholar
  7. 7.
    McCarthy, J.: Circumscription — a form of nonmonotonic reasoning. Artificial Intelligence 13(1-2), 27–39 (1980)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Reiter, R.: A logic for default reasoning. Artificial Intelligence 13(1-2), 81–132 (1980)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Kowalski, R., Bowen, K. (eds.) Proceedings of the International Conference on Logic Programming, pp. 1070–1080. MIT Press, Cambridge (1988)Google Scholar
  10. 10.
    Eiter, T., Leone, N., Mateis, C., Pfeifer, G., Scarcello, F.: A deductive system for non-monotonic reasoning. In: Dix, J., Furbach, U., Nerode, A. (eds.) Proceedings of the International Conference on Logic Programming and Nonmonotonic Reasoning, pp. 364–375. Springer, Heidelberg (1997)Google Scholar
  11. 11.
    Niemelä, I., Simons, P.: Smodels — an implementation of the stable model and well-founded semantics for normal logic programs. In: Dix, J., Furbach, U., Nerode, A. (eds.) Proceedings of the International Conference on Logic Programming and Nonmonotonic Reasoning, pp. 420–429. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  12. 12.
    Lifschitz, V.: Answer Set Planning. In: De Schreye, D. (ed.) Proceedings of the International Conference on Logic Programming, pp. 23–37. MIT Press, Cambridge (1999)Google Scholar
  13. 13.
    Borchert, P., Anger, C., Schaub, T., Truszczyński, M.: Towards Systematic Benchmarking in Answer Set Programming: The Dagstuhl Initiative. In: Lifschitz, V., Niemelä, I. (eds.) LPNMR 2004. LNCS (LNAI), vol. 2923, pp. 3–7. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Gebser, M., Liu, L., Namasivayam, G., Neumann, A., Schaub, T., Truszczyński, M.: The first answer set programming system competition. In: Baral, C., Brewka, G., Schlipf, J. (eds.) LPNMR 2007. LNCS (LNAI), vol. 4483, pp. 3–17. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Biere, A., Heule, M., van Maaren, H., Walsh, T. (eds.): Handbook of Satisfiability. IOS Press, Amsterdam (2009)MATHGoogle Scholar
  16. 16.
    Nieuwenhuis, R., Oliveras, A., Tinelli, C.: Solving SAT and SAT modulo theories: From an abstract Davis-Putnam-Logemann-Loveland procedure to DPLL(T). Journal of the ACM 53(6), 937–977 (2006)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Rossi, F., van Beek, P., Walsh, T. (eds.): Handbook of Constraint Programming. Elsevier, Amsterdam (2006)MATHGoogle Scholar
  18. 18.
    Denecker, M., Kakas, A.C.: Abduction in Logic Programming. In: Kakas, A.C., Sadri, F. (eds.) Computational Logic: Logic Programming and Beyond. LNCS (LNAI), vol. 2407, pp. 402–436. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  19. 19.
    Mitchell, D., Ternovska, E.: A framework for representing and solving NP search problems. In: Veloso, M., Kambhampati, S. (eds.) Proceedings of the National Conference on Artificial Intelligence, pp. 430–435. AAAI Press / MIT Press (2005)Google Scholar
  20. 20.
    Kakas, A., Michael, A.: Air-Crew scheduling through abduction. In: Imam, I., Kodratoff, Y., El-Dessouki, A., Ali, M. (eds.) Proceedings of the International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, pp. 600–611. Springer, Heidelberg (1999)Google Scholar
  21. 21.
    Pelov, N., De Mot, E., Denecker, M.: Logic programming approaches for representing and solving constraint satisfaction problems: A comparison. In: Parigot, M., Voronkov, A. (eds.) LPAR 2000. LNCS (LNAI), vol. 1955, pp. 225–239. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  22. 22.
    Van Hentenryck, P.: Constraint Satisfaction in Logic Programming. MIT Press, Cambridge (1989)Google Scholar
  23. 23.
  24. 24.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marc Denecker
    • 1
  • Joost Vennekens
    • 1
  • Stephen Bond
    • 1
  • Martin Gebser
    • 2
  • Mirosław Truszczyński
    • 3
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenHeverlee
  2. 2.Institut für InformatikUniversität PotsdamPotsdamGermany
  3. 3.Department of Computer ScienceUniversity of KentuckyLexingtonUSA

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