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Rewriting recursive aggregates in answer set programming: back to monotonicity

Published online by Cambridge University Press:  03 September 2015

MARIO ALVIANO
Affiliation:
University of Calabria, Italy
WOLFGANG FABER
Affiliation:
University of Huddersfield, UK
MARTIN GEBSER
Affiliation:
Aalto University, HIIT, Finland

Abstract

Aggregation functions are widely used in answer set programming for representing and reasoning on knowledge involving sets of objects collectively. Current implementations simplify the structure of programs in order to optimize the overall performance. In particular, aggregates are rewritten into simpler forms known as monotone aggregates. Since the evaluation of normal programs with monotone aggregates is in general on a lower complexity level than the evaluation of normal programs with arbitrary aggregates, any faithful translation function must introduce disjunction in rule heads in some cases. However, no function of this kind is known. The paper closes this gap by introducing a polynomial, faithful, and modular translation for rewriting common aggregation functions into the simpler form accepted by current solvers. A prototype system allows for experimenting with arbitrary recursive aggregates, which are also supported in the recent version 4.5 of the grounder gringo, using the methods presented in this paper.

Type
Regular Papers
Copyright
Copyright © Cambridge University Press 2015 

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References

Abseher, M., Bliem, B., Charwat, G., Dusberger, F. and Woltran, S. 2014. Computing secure sets in graphs using answer set programming. In Seventh International Workshop on Answer Set Programming and Other Computing Paradigms (ASPOCP 2014), Inclezan, D. and Maratea, M., Eds.CrossRefGoogle Scholar
Alviano, M., Calimeri, F., Faber, W., Leone, N. and Perri, S. 2011. Unfounded sets and well-founded semantics of answer set programs with aggregates. Journal of Artificial Intelligence Research 42, 487527.Google Scholar
Alviano, M., Dodaro, C. and Ricca, F. 2014. Anytime computation of cautious consequences in answer set programming. Theory and Practice of Logic Programming 14, 4–5, 755770.CrossRefGoogle Scholar
Alviano, M. and Faber, W. 2013. The complexity boundary of answer set programming with generalized atoms under the FLP semantics. In Twelfth International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2013), Cabalar, P. and Son, T., Eds. Lecture Notes in Computer Science, vol. 8148. Springer, 6772.Google Scholar
Alviano, M., Faber, W., Leone, N., Perri, S., Pfeifer, G. and Terracina, G. 2010. The disjunctive Datalog system DLV. In First International Workshop on Datalog 2.0 (Datalog Reloaded), de Moor, O., Gottlob, G., Furche, T., and Sellers, A., Eds. Lecture Notes in Computer Science, vol. 6702. Springer, 282301.Google Scholar
Bartholomew, M., Lee, J. and Meng, Y. 2011. First-order semantics of aggregates in answer set programming via modified circumscription. In AAAI 2011 Spring Symposium on Logical Formalizations of Commonsense Reasoning (SS-11-06), Davis, E., Doherty, P., and Erdem, E., Eds. AAAI, 1622.Google Scholar
Berman, P., Karpinski, M., Larmore, L., Plandowski, W. and Rytter, W. 2002. On the complexity of pattern matching for highly compressed two-dimensional texts. Journal of Computer and System Sciences 65, 2, 332350.CrossRefGoogle Scholar
Bomanson, J., Gebser, M. and Janhunen, T. 2014. Improving the normalization of weight rules in answer set programs. In Fourteenth European Conference on Logics in Artificial Intelligence (JELIA 2014), Fermé, E. and Leite, J., Eds. Lecture Notes in Computer Science, vol. 8761. Springer, 166180.CrossRefGoogle Scholar
Bomanson, J. and Janhunen, T. 2013. Normalizing cardinality rules using merging and sorting constructions. In Twelfth International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2013), Cabalar, P. and Son, T., Eds. Lecture Notes in Computer Science, vol. 8148. Springer, 187199.Google Scholar
Brewka, G., Eiter, T. and Truszczyński, M. 2011. Answer set programming at a glance. Communications of the ACM 54, 12, 92103.CrossRefGoogle Scholar
Eiter, T., Fink, M., Krennwallner, T. and Redl, C. 2012. Conflict-driven ASP solving with external sources. Theory and Practice of Logic Programming 12, 4–5, 659679.CrossRefGoogle Scholar
Eiter, T., Fink, M., Krennwallner, T., Redl, C. and Schüller, P. 2014. Efficient HEX-program evaluation based on unfounded sets. Journal of Artificial Intelligence Research 49, 269321.CrossRefGoogle Scholar
Eiter, T. and Gottlob, G. 1995. On the computational cost of disjunctive logic programming: Propositional case. Annals of Mathematics and Artificial Intelligence 15, 3–4, 289323.CrossRefGoogle Scholar
Eiter, T., Ianni, G., Lukasiewicz, T., Schindlauer, R. and Tompits, H. 2008. Combining answer set programming with description logics for the semantic web. Artificial Intelligence 172, 12–13, 14951539.CrossRefGoogle Scholar
Eiter, T., Tompits, H. and Woltran, S. 2005. On solution correspondences in answer set programming. In Nineteenth International Joint Conference on Artificial Intelligence (IJCAI 2005), Kaelbling, L. and Saffiotti, A., Eds. Professional Book Center, 97102.Google Scholar
Faber, W., Pfeifer, G. and Leone, N. 2011. Semantics and complexity of recursive aggregates in answer set programming. Artificial Intelligence 175, 1, 278298.CrossRefGoogle Scholar
Faber, W., Pfeifer, G., Leone, N., Dell'Armi, T. and Ielpa, G. 2008. Design and implementation of aggregate functions in the DLV system. Theory and Practice of Logic Programming 8, 5–6, 545580.CrossRefGoogle Scholar
Ferraris, P. 2011. Logic programs with propositional connectives and aggregates. ACM Transactions on Computational Logic 12, 4, 25:125:44.CrossRefGoogle 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., Kaminski, R., König, A. and Schaub, T. 2011. Advances in gringo series 3. In Eleventh International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2011), Delgrande, J. and Faber, W., Eds. Lecture Notes in Computer Science, vol. 6645. Springer, 345351.Google Scholar
Gebser, M., Kaufmann, B. and Schaub, T. 2012. Conflict-driven answer set solving: From theory to practice. Artificial Intelligence 187/188, 5289.CrossRefGoogle Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Fifth International Conference and Symposium on Logic Programming (ICLP 1988), Kowalski, R. and Bowen, K., Eds. MIT Press, 10701080.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 3–4, 365385.CrossRefGoogle Scholar
Gelfond, M. and Zhang, Y. 2014. Vicious circle principle and logic programs with aggregates. Theory and Practice of Logic Programming 14, 4–5, 587601.CrossRefGoogle Scholar
Giunchiglia, E., Lierler, Y. and Maratea, M. 2006. Answer set programming based on propositional satisfiability. Journal of Automated Reasoning 36, 4, 345377.CrossRefGoogle Scholar
Janhunen, T. 2006. Some (in)translatability results for normal logic programs and propositional theories. Journal of Applied Non-Classical Logics 16, 1–2, 3586.CrossRefGoogle Scholar
Janhunen, T. and Niemelä, I. 2012. Applying visible strong equivalence in answer-set program transformations. In Correct Reasoning: Essays on Logic-Based AI in Honour of Vladimir Lifschitz, Erdem, E., Lee, J., Lierler, Y., and Pearce, D., Eds. Lecture Notes in Computer Science, vol. 7265. Springer, 363379.CrossRefGoogle Scholar
Lifschitz, V., Pearce, D. and Valverde, A. 2001. Strongly equivalent logic programs. ACM Transactions on Computational Logic 2, 4, 526541.CrossRefGoogle Scholar
Liu, G. and You, J. 2013. Relating weight constraint and aggregate programs: Semantics and representation. Theory and Practice of Logic Programming 13, 1, 131.CrossRefGoogle Scholar
Liu, L., Pontelli, E., Son, T. and Truszczyński, 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 27, 299334.CrossRefGoogle 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, 3, 301353.CrossRefGoogle Scholar
Shen, Y., Wang, K., Eiter, T., Fink, M., Redl, C., Krennwallner, T. and Deng, J. 2014. FLP answer set semantics without circular justifications for general logic programs. Artificial Intelligence 213, 141.CrossRefGoogle Scholar
Simons, P., Niemelä, I. and Soininen, T. 2002. Extending and implementing the stable model semantics. Artificial Intelligence 138, 1–2, 181234.CrossRefGoogle Scholar
Son, T. and Pontelli, E. 2007. A constructive semantic characterization of aggregates in answer set programming. Theory and Practice of Logic Programming 7, 3, 355375.CrossRefGoogle Scholar
Turner, H. 2003. Strong equivalence made easy: Nested expressions and weight constraints. Theory and Practice of Logic Programming 3, 4–5, 609622.CrossRefGoogle Scholar
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