AI*IA 2013: AI*IA 2013: Advances in Artificial Intelligence pp 73-84 | Cite as
Automated Selection of Grounding Algorithm in Answer Set Programming
Conference paper
Abstract
Answer Set Programming (ASP) is a powerful language for knowledge representation and reasoning. ASP is exploited in real-world applications and is also attracting the interest of industry thanks to the availability of efficient implementations. ASP systems compute solutions relying on two modules: a grounder that produces, by removing variables from the rules, a ground program equivalent to the input one; and a model generator (or solver) that computes the solutions of such propositional program. In this paper we make a first step toward the exploitation of automated selection techniques to the grounding module. We rely on two well-known ASP grounders, namely the grounder of the DLV system and GrinGo and we leverage on automated classification algorithms to devise and implement an automatic procedure for selecting the “best” grounder for each problem instance. An experimental analysis, conducted on benchmarks and solvers from the 3rd ASP Competition, shows that our approach improves the evaluation performance independently from the solver associated with our grounder selector.
Keywords
Logic Program Conjunctive Query Ground Instance Strongly Connect Component Grounding Algorithm
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.
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References
- 1.Alviano, M., Faber, W., Greco, G., Leone, N.: Magic sets for disjunctive datalog programs. Artificial Intellegence 187, 156–192 (2012)MathSciNetCrossRefGoogle Scholar
- 2.Alviano, M., Faber, W., Leone, N.: Disjunctive asp with functions: Decidable queries and effective computation. Theory and Practice of Logic Programming 10(4-6), 497–512 (2010)MathSciNetCrossRefMATHGoogle Scholar
- 3.Calimeri, F., Ianni, G., Ricca, F.: The third answer set programming system competition (2011), https://www.mat.unical.it/aspcomp2011/
- 4.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
- 5.Davis, M., Putnam, H.: A Computing Procedure for Quantification Theory. Journal of the ACM 7, 201–215 (1960)MathSciNetCrossRefMATHGoogle Scholar
- 6.Eiter, T., Gottlob, G., Mannila, H.: Disjunctive Datalog. ACM Transactions on Database Systems 22(3), 364–418 (1997)CrossRefGoogle Scholar
- 7.Faber, W., Leone, N., Pfeifer, G.: Recursive aggregates in disjunctive logic programs: Semantics and complexity. In: Alferes, J.J., Leite, J. (eds.) JELIA 2004. LNCS (LNAI), vol. 3229, pp. 200–212. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 8.Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proceedings of ICML 1998, p. 144. Morgan Kaufmann Pub. (1998)Google Scholar
- 9.Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T., Schneider, M.T., Ziller, S.: A portfolio solver for answer set programming: Preliminary report. In: Delgrande, J.P., Faber, W. (eds.) LPNMR 2011. LNCS, vol. 6645, pp. 352–357. Springer, Heidelberg (2011)CrossRefGoogle Scholar
- 10.Gebser, M., Kaminski, R., König, A., Schaub, T.: Advances in gringo series 3. In: Delgrande, J.P., Faber, W. (eds.) LPNMR 2011. LNCS, vol. 6645, pp. 345–351. Springer, Heidelberg (2011)CrossRefGoogle Scholar
- 11.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, Hyderabad (2007)Google Scholar
- 12.Gebser, M., Schaub, T., Thiele, S.: GrinGo: A New Grounder for Answer Set Programming. In: Baral, C., Brewka, G., Schlipf, J. (eds.) LPNMR 2007. LNCS (LNAI), vol. 4483, pp. 266–271. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 13.Gelfond, M., Lifschitz, V.: The Stable Model Semantics for Logic Programming. In: Logic Programming: Proceedings Fifth Intl Conference and Symposium, pp. 1070–1080. MIT Press (1988)Google Scholar
- 14.Gelfond, M., Lifschitz, V.: Classical Negation in Logic Programs and Disjunctive Databases. New Generation Computing 9, 365–385 (1991)CrossRefGoogle Scholar
- 15.Grasso, G., Leone, N., Manna, M., Ricca, F.: ASP at work: Spin-off and applications of the DLV system. In: Balduccini, M., Son, T.C. (eds.) Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning. LNCS (LNAI), vol. 6565, pp. 432–451. Springer, Heidelberg (2011)CrossRefGoogle Scholar
- 16.Janhunen, T.: Some (in)translatability results for normal logic programs and propositional theories. Journal of Applied Non-Classical Logics 16, 35–86 (2006)MathSciNetCrossRefMATHGoogle Scholar
- 17.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)CrossRefGoogle Scholar
- 18.Leone, N., Perri, S., Scarcello, F.: Improving ASP instantiators by join-ordering methods. In: Eiter, T., Faber, W., Truszczyński, M. (eds.) LPNMR 2001. LNCS (LNAI), vol. 2173, pp. 280–294. Springer, Heidelberg (2001)Google Scholar
- 19.Leone, N., Perri, S., Scarcello, F.: BackJumping Techniques for Rules Instantiation in the DLV System. In: Proc. of NMR 2004, pp. 258–266 (2004)Google Scholar
- 20.Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., Scarcello, F.: The DLV System for Knowledge Representation and Reasoning. ACM Transactions on Computational Logic 7(3), 499–562 (2006)MathSciNetCrossRefGoogle Scholar
- 21.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)CrossRefGoogle Scholar
- 22.Maratea, M., Pulina, L., Ricca, F.: The multi-engine ASP solver me-asp. In: del Cerro, L.F., Herzig, A., Mengin, J. (eds.) JELIA 2012. LNCS, vol. 7519, pp. 484–487. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 23.Maratea, M., Pulina, L., Ricca, F.: A Multi-Engine approach to Answer Set Programming. Theory and Practice of Logic Programming (in press)Google Scholar
- 24.Mariën, M., Wittocx, J., Denecker, M., Bruynooghe, M.: Sat(id): Satisfiability of propositional logic extended with inductive definitions. In: Kleine Büning, H., Zhao, X. (eds.) SAT 2008. LNCS, vol. 4996, pp. 211–224. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 25.Perri, S., Scarcello, F., Catalano, G., Leone, N.: Enhancing DLV instantiator by backjumping techniques. Annals of Mathematics and Artificial Intelligence 51(2-4), 195–228 (2007)MathSciNetCrossRefMATHGoogle Scholar
- 26.Pulina, L., Tacchella, A.: A self-adaptive multi-engine solver for quantified boolean formulas. Constraints 14(1), 80–116 (2009)MathSciNetCrossRefMATHGoogle Scholar
- 27.Simons, P., Niemelä, I., Soininen, T.: Extending and Implementing the Stable Model Semantics. Artificial Intelligence 138, 181–234 (2002)MathSciNetCrossRefMATHGoogle Scholar
- 28.Syrjänen, T.: Lparse 1.0 User’s Manual (2002), http://www.tcs.hut.fi/Software/smodels/lparse.ps.gz
- 29.Ullman, J.D.: Principles of Database and Knowledge Base Systems. Computer Science Press (1989)Google Scholar
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