WASP: A Native ASP Solver Based on Constraint Learning

  • Mario Alviano
  • Carmine Dodaro
  • Wolfgang Faber
  • Nicola Leone
  • Francesco Ricca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8148)


This paper introduces WASP, an ASP solver handling disjunctive logic programs under the stable model semantics. WASP implements techniques originally introduced for SAT solving that have been extended to cope with ASP programs. Among them are restarts, conflict-driven constraint learning and backjumping. Moreover, WASP combines these SAT-based techniques with optimization methods that have been specifically designed for ASP computation, such as source pointers enhancing unfounded-sets computation, forward and backward inference operators based on atom support, and techniques for stable model checking. Concerning the branching heuristics, WASP adopts the BerkMin criterion hybridized with look-ahead techniques. The paper also reports on the results of experiments, in which WASP has been run on the system track of the third ASP Competition.


Stable Model Semantic Disjunctive Logic Program Implication Graph Backtrack Search Algorithm Propositional Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Gelfond, M., Lifschitz, V.: Classical Negation in Logic Programs and Disjunctive Databases. New Generation Computing 9, 365–385 (1991)CrossRefGoogle Scholar
  2. 2.
    Eiter, T., Gottlob, G., Mannila, H.: Disjunctive Datalog. ACM Transactions on Database Systems 22, 364–418 (1997)CrossRefGoogle Scholar
  3. 3.
    Alviano, M., Faber, W., Leone, N., Perri, S., Pfeifer, G., Terracina, G.: The disjunctive datalog system DLV. In: de Moor, O., Gottlob, G., Furche, T., Sellers, A. (eds.) Datalog 2010. LNCS, vol. 6702, pp. 282–301. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Gebser, M., Kaufmann, B., Neumann, A., Schaub, T.: Conflict-driven answer set solving. In: Twentieth International Joint Conference on Artificial Intelligence, IJCAI 2007, pp. 386–392. Morgan Kaufmann Publishers (2007)Google Scholar
  5. 5.
    Lierler, Y., Maratea, M.: Cmodels-2: SAT-based Answer Set Solver Enhanced to Non-tight Programs. In: Lifschitz, V., Niemelä, I. (eds.) LPNMR 2004. LNCS (LNAI), vol. 2923, pp. 346–350. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Grasso, G., Iiritano, S., Leone, N., Ricca, F.: Some DLV applications for knowledge management. In: Erdem, E., Lin, F., Schaub, T. (eds.) LPNMR 2009. LNCS, vol. 5753, pp. 591–597. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Ricca, F., Grasso, G., Alviano, M., Manna, M., Lio, V., Iiritano, S., Leone, N.: Team-building with answer set programming in the gioia-tauro seaport. Theory and Practice of Logic Programming 12, 361–381 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Manna, M., Oro, E., Ruffolo, M., Alviano, M., Leone, N.: The HiLeX system for semantic information extraction. In: Hameurlain, A., Küng, J., Wagner, R. (eds.) TLDKS V. LNCS, vol. 7100, pp. 91–125. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Ricca, F., Alviano, M., Dimasi, A., Grasso, G., Ielpa, S.M., Iiritano, S., Manna, M., Leone, N.: A Logic-Based System for e-Tourism. Fundamenta Informaticae 105, 35–55 (2010)MathSciNetGoogle Scholar
  10. 10.
    Calimeri, F., et al.: The Third Answer Set Programming Competition: Preliminary Report of the System Competition Track. In: Delgrande, J.P., Faber, W. (eds.) LPNMR 2011. LNCS, vol. 6645, pp. 388–403. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Davis, M., Logemann, G., Loveland, D.: A Machine Program for Theorem Proving. Communications of the ACM 5, 394–397 (1962)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Zhang, L., Madigan, C.F., Moskewicz, M.W., Malik, S.: Efficient Conflict Driven Learning in Boolean Satisfiability Solver. In: Proceedings of ICCAD 2001, pp. 279–285 (2001)Google Scholar
  13. 13.
    Gaschnig, J.: Performance measurement and analysis of certain search algorithms. PhD thesis, Carnegie Mellon University, Pittsburgh, PA, USA (1979) TR CMU-CS-79-124Google Scholar
  14. 14.
    Gomes, C.P., Selman, B., Kautz, H.A.: Boosting Combinatorial Search Through Randomization. In: Proceedings of AAAI/IAAI 1998, pp. 431–437. AAAI Press (1998)Google Scholar
  15. 15.
    Moskewicz, M.W., Madigan, C.F., Zhao, Y., Zhang, L., Malik, S.: Chaff: Engineering an Efficient SAT Solver. In: Proceedings of DAC 2001, pp. 530–535. ACM (2001)Google Scholar
  16. 16.
    Goldberg, E., Novikov, Y.: BerkMin: A Fast and Robust Sat-Solver. In: Design, Automation and Test in Europe Conference and Exposition, DATE 2002, Paris, France, pp. 142–149. IEEE Computer Society (2002)Google Scholar
  17. 17.
    Faber, W., Leone, N., Pfeifer, G.: Pushing Goal Derivation in DLP Computations. In: Gelfond, M., Leone, N., Pfeifer, G. (eds.) LPNMR 1999. LNCS (LNAI), vol. 1730, pp. 177–191. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  18. 18.
    Simons, P., Niemelä, I., Soininen, T.: Extending and Implementing the Stable Model Semantics. Artificial Intelligence 138, 181–234 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Koch, C., Leone, N., Pfeifer, G.: Enhancing Disjunctive Logic Programming Systems by SAT Checkers. Artificial Intelligence 15, 177–212 (2003)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Eén, N., Sörensson, N.: An Extensible SAT-solver. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 502–518. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  21. 21.
    Ben-Eliyahu, R., Dechter, R.: Propositional Semantics for Disjunctive Logic Programs. Annals of Mathematics and Artificial Intelligence 12, 53–87 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Luby, M., Sinclair, A., Zuckerman, D.: Optimal speedup of las vegas algorithms. Inf. Process. Lett. 47, 173–180 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Goldberg, E., Novikov, Y.: Berkmin: A fast and robust sat-solver. Discrete Appl. Math. 155, 1549–1561 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Gebser, M., Schaub, T.: Tableau calculi for answer set programming. In: Etalle, S., Truszczyński, M. (eds.) ICLP 2006. LNCS, vol. 4079, pp. 11–25. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  25. 25.
    Ward, J., Schlipf, J.: Answer Set Programming with Clause Learning. In: Lifschitz, V., Niemelä, I. (eds.) LPNMR 2004. LNCS (LNAI), vol. 2923, pp. 302–313. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  26. 26.
    Ricca, F., Faber, W., Leone, N.: A Backjumping Technique for Disjunctive Logic Programming. AI Communications 19, 155–172 (2006)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mario Alviano
    • 1
  • Carmine Dodaro
    • 1
  • Wolfgang Faber
    • 1
  • Nicola Leone
    • 1
  • Francesco Ricca
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of CalabriaRendeItaly

Personalised recommendations