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Relating weight constraint and aggregate programs: Semantics and representation

Published online by Cambridge University Press:  30 June 2011

GUOHUA LIU
Affiliation:
University of Alberta, Edmonton T6G 2R3, Canada (e-mail: [email protected], [email protected])
JIA-HUAI YOU
Affiliation:
University of Alberta, Edmonton T6G 2R3, Canada (e-mail: [email protected], [email protected])

Abstract

Weight constraint and aggregate programs are among the most widely used logic programs with constraints. In this paper, we relate the semantics of these two classes of programs, namely, the stable model semantics for weight constraint programs and the answer set semantics based on conditional satisfaction for aggregate programs. Both classes of programs are instances of logic programs with constraints, and in particular, the answer set semantics for aggregate programs can be applied to weight constraint programs. We show that the two semantics are closely related. First, we show that for a broad class of weight constraint programs, called strongly satisfiable programs, the two semantics coincide. When they disagree, a stable model admitted by the stable model semantics may be circularly justified. We show that the gap between the two semantics can be closed by transforming a weight constraint program to a strongly satisfiable one so that no circular models may be generated under the current implementation of the stable model semantics. We further demonstrate the close relationship between the two semantics by formulating a transformation from weight constraint programs to logic programs with nested expressions, which preserves the answer set semantics. Our study on the semantics leads to an investigation of a methodological issue, namely, the possibility of compact representation of aggregate programs by weight constraint programs. We show that almost all standard aggregates can be encoded by weight constraints compactly. This makes it possible to compute the answer sets of aggregate programs using the answer set programming solvers for weight constraint programs. This approach is compared experimentally with the ones where aggregates are handled more explicitly, which show that the weight constraint encoding of aggregates enables a competitive approach to answer set computation for aggregate programs.

Type
Regular Papers
Copyright
Copyright © Cambridge University Press 2011

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References

Armi, D., Faber, W. and Ielpa, G. 2003. Aggregate functions in disjunctive logic programming: Semantics, complexity, and implementation in DLV*. In Proc. of International Joint Conference on Artificial Intelligence (IJCAI '03), 847–852.Google Scholar
Balduccini, M., Gelfond, M., Watson, R. and Nogueira, M. 2001. The USA-advisor: A case study in answer set planning. In Proc. of Logic Programming and Non-Monotonic Reasoning (LPNMR), 439–442.Google Scholar
Caldiran, O., Haspalamutgil, K., Ok, A., Palaz, C., Erdem, E. and Patoglu, V. 2009. Bridging the gap between high-level reasoning and low-level control. In Proc. of Logic Programming and Non-Monotonic Reasoning (LPNMR), 342–354.Google Scholar
Calimeri, F., Faber, W., Leone, N. and Perri, S. 2005. Declarative and computational properties of logic programs with aggregates. In Proc. of International Joint Conference on Artificial Intelligence (IJCAI '05), 406–411.Google Scholar
Delgrande, J. P., Grote, T. and Hunter, A. 2009. A general approach to the verification of cryptographic protocols using answer set programming. In Proc. of Logic Programming and Non-Monotonic Reasoning (LPNMR), 355–367.Google Scholar
Denecker, M., Pelov, N. and Bruynooghe, M. 2001. Ultimate well-founded and stable semantics for logic programs with aggregates. In Proc. of International Conference on Logic Programming (ICLP '01), 212–226.Google Scholar
Denecker, M., Vennekens, J., Bond, S., Gebser, M. and Truszczynski, M. 2009. The second answer set programming competition. In Proc. of Logic Programming and Non-Monotonic Reasoning (LPNMR), 637–654.Google Scholar
Elkabani, I., Pontelli, E. and Son, T. C. 2005. SmodelsA—A system for computing answer sets of logic programs with aggregates. In Proc. of Logic Programming and Non-Monotonic Reasoning (LPNMR '05), 427–431.Google Scholar
Erdem, E., Lin, F. and Schaub, T., Eds., 2009. Session 3. Original Application Papers. In Proc. of Logic Programming and Non-Monotonic Reasoning (LPNMR '09). Springer, 342395.CrossRefGoogle Scholar
Faber, W., Leone, N. and Pfeifer, G. 2004. Recursive aggregates in disjunctive logic programs. In Proc. of European Conference on Logics in Artificial Intelligence (JELIA '04), 200–212.Google 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. 2005. Answer sets for propositional theories. In Proc. Logic Programming and Non-Monotonic Reasoning (LPNMR '05), 119–131.Google Scholar
Ferraris, P. and Lifschitz, V. 2005. Weight constraints as nested expressions. Theory and Practice of Logic Programming 5 (1–2), 4574.CrossRefGoogle Scholar
Gebser, M., Kaufmann, B., Neumann, A. and Schaub, T. 2007a. Conflict-driven answer set solving. In Proc. of International Joint Conference on Artificial Intelligence (IJCAI '07), 386–392.Google Scholar
Gebser, M., Liu, L., Namasivayam, G., Neumann, A., Schaub, T. and Truszczyński, M. 2007b. The first answer set programming system competition. In Proc. of Logic Programming and Non-Monotonic Reasoning (LPNMR '07), 1–17.Google Scholar
Gelfond, M. 2008. Answer sets. In Handbook of Knowledge Representation. Elsevier, Chapter 1, 285316.CrossRefGoogle Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Proc. of International Conference on Logic Programming (ICLP), 1070–1080.Google Scholar
Giunchiglia, E., Lierler, Y. and Maratea, M. 2006. Answer set programming based on propositional satisfiability. Journal of Automated Reasoning 36 (4), 345377.Google Scholar
Ielpa, S. M., Iiritano, S., Leone, N. and Ricca, F. 2009. An ASP-based system for e-tourism. In Proc. of Logic Programming and Non-Monotonic Reasoning (LPNMR), 368–381.Google Scholar
Lifschits, V., Tang, L. and Turner, H. 1999. Nested expressions in logic programs. Annals of Mathematics and Artificial Intelligence 25, 369389.CrossRefGoogle Scholar
Liu, L., Pontelli, E., Son, T. and Truszczynski, M. 2010. Logic programs with abstract constraint atoms: the role of computations. Artificial Intelligence 174 (3–4), 295315.Google Scholar
Liu, L. and Truszczyński, M. 2006. Properties and applications of programs with monotone and convex constraints. Journal of Artificial Intelligence Research 7, 299334.Google Scholar
Liu, G. and You, J. 2008. Lparse programs revisited: semantics and representation of aggregates. In Proc. of International Conference on Logic Programming (ICLP '08), 347–361.Google Scholar
Marek, V., Niemelä, I. and Truszczyński, M. 2007. Logic programs with monotone abstract constraint atoms. Theory and Practice of Logic Programming 8 (2), 167199.Google Scholar
Marek, V. and Truszczyński, M. 2004. Logic programs with abstract constraint atoms. In Proc. of Association for the Advancement of Artificial Intelligence (AAAI '04), 86–91.Google Scholar
Marek, V. W. and Remmel, J. B. 2004. Set constraints in logic programming. In Proc. of Logic Programming and Non-Monotonic Reasoning (LPNMR '04), 167–179.Google Scholar
Niemelä, I. 1999. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Math. and Artificial Intelligence 25 (3–4), 241273.Google Scholar
Oetsch, J., Seidl, M., Tompits, H. and Woltran, S. 2009. cct on stage: Generalised uniform equivalence testing for verifying student assignment solutions. In Proc. of Logic Programming and Non-Monotonic Reasoning (LPNMR), 382–395.Google Scholar
Pelov, N., Denecker, M. and Bruynooghe, M. 2003. Translation of aggregate programs to normal logic programs. In Proc. of Answer Set Programming (ASP '03), 29–42.Google Scholar
Pelov, N., Denecker, M. and Bruynooghe, M. 2004. Partial stable models for logic programs with aggregates. In Proc. of Logic Programming and Non-Monotonic Reasoning (LPNMR '04), 207–219.Google Scholar
Pelov, N., Denecker, M. and Bruynooghe, M. 2007. Well-founded and stable semantics of logic programs with aggregates. Theory and Practice of Logic Programming 7, 301353.Google Scholar
Shen, Y., You, J. and Yuan, L. 2009. Characterizations of stable model semantics for logic programs with arbitrary constraint atoms. Theory and Practice of Logic Programming 9 (4), 529564.CrossRefGoogle Scholar
Simons, P., Niemelä, I. and Soininen, T. 2002. Extending and implementing the stable model semantics. Artificial Intelligence 138 (1–2), 181234.Google Scholar
Son, T. C. and Pontelli, E. 2007. A constructive semantic characterization of aggregates in answer set programming. Theory and Practice of Logic Programming 7, 355375.Google Scholar
Son, T. C., Pontelli, E. and Tu, P. H. 2007. Answer sets for logic programs with arbitrary abstract constraint atoms. Journal of Artificial Intelligence Research 29, 353389.CrossRefGoogle Scholar
van Gelder, A., Ross, K. and Schlipf, J. S. 1991. The well-founded semantics for general logic programs. Journal of the ACM 38 (3), 620650.Google Scholar
Wu, G., You, J. and Lin, G. 2007. Quartet based phylogeny reconstruction with answer set programming. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4 (1), 139152.Google Scholar
You, J., Yuan, L. Y., Liu, G. and Shen, Y. 2007. Logic programs with abstract constraints: Representation, disjunction and complexities. In Proc. of Logic Programming and Non-Monotonic Reasoning (LPNMR '07), 228–240.Google Scholar