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Constraint Answer Set Programming without Grounding

Published online by Cambridge University Press:  10 August 2018

JOAQUIN ARIAS
Affiliation:
IMDEA Software Institute and Universidad Politécnica de Madrid (e-mail: [email protected], [email protected], [email protected], [email protected])
MANUEL CARRO
Affiliation:
IMDEA Software Institute and Universidad Politécnica de Madrid (e-mail: [email protected], [email protected], [email protected], [email protected])
ELMER SALAZAR
Affiliation:
University of Texas at Dallas (e-mail: [email protected], [email protected], [email protected])
KYLE MARPLE
Affiliation:
University of Texas at Dallas (e-mail: [email protected], [email protected], [email protected])
GOPAL GUPTA
Affiliation:
University of Texas at Dallas (e-mail: [email protected], [email protected], [email protected])
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Abstract

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Extending ASP with constraints (CASP) enhances its expressiveness and performance. This extension is not straightforward as the grounding phase, present in most ASP systems, removes variables and the links among them, and also causes a combinatorial explosion in the size of the program. Several methods to overcome this issue have been devised: restricting the constraint domains (e.g., discrete instead of dense), or the type (or number) of models that can be returned. In this paper we propose to incorporate constraints into s(ASP), a goal-directed, top-down execution model which implements ASP while retaining logical variables both during execution and in the answer sets. The resulting model, s(CASP), can constrain variables that, as in CLP, are kept during the execution and in the answer sets. s(CASP) inherits and generalizes the execution model of s(ASP) and is parametric w.r.t. the constraint solver. We describe this novel execution model and show through several examples the enhanced expressiveness of s(CASP) w.r.t. ASP, CLP, and other CASP systems. We also report improved performance w.r.t. other very mature, highly optimized ASP systems in some benchmarks.

Type
Original Article
Copyright
Copyright © Cambridge University Press 2018 

Footnotes

*Work partially supported by EIT Digital (https://eitdigital.eu), MINECO project TIN2015-67522-C3-1-R (TRACES), Comunidad de Madrid project S2013/ICE-2731 N-Greens Software, NSF IIS 1718945, and NSF IIS 1423419.

References

Alferes, J. J., Pereira, L. M., and Swift, T. 2004. Abduction in Well-Founded Semantics and Generalized Stable Models via Tabled Dual Programs. Theory and Practice of Logic Programming 4, 4, 383428.Google Scholar
Alviano, M., Faber, W., Greco, G., and Leone, N. 2012. Magic Sets for Disjunctive Datalog Programs. Artificial Intelligence 187, 156192.Google Scholar
Arias, J. 2016. Tabled CLP for Reasoning over Stream Data. In Technical Communications of the 32nd Int'l Conference on Logic Programming (ICLP'16). Vol. 52. OASIcs, 1–8. Doctoral Consortium.Google Scholar
Arias, J. and Carro, M. 2016. Description and Evaluation of a Generic Design to Integrate CLP and Tabled Execution. In 18th Int'l. ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming (PPDP'16). ACM Press, 10–23.Google Scholar
Balduccini, M. and Lierler, Y. 2017. Constraint Answer Set Solver EZCSP and why Integration Schemas Matter. Theory and Practice of Logic Programming 17, 4, 462515.Google Scholar
Banbara, M., Kaufmann, B., Ostrowski, M., and Schaub, T. 2017. Clingcon: The Next Generation. Theory and Practice of Logic Programming 17, 4, 408461.Google Scholar
Baselice, S. and Bonatti, P. A. 2010. A Decidable Subclass of Finitary Programs. Theory and Practice of Logic Programming 10, 4–6, 481496.Google Scholar
Baselice, S., Bonatti, P. A., and Criscuolo, G. 2009. On Finitely Recursive Programs. Theory and Practice of Logic Programming 9, 2, 213238.Google Scholar
Brewka, G., Eiter, T., and Truszczyński, M. 2011. Answer Set Programming at a Glance. Communications of the ACM 54, 12, 92103.Google Scholar
Calimeri, F., Cozza, S., and Ianni, G. 2007. External Sources of Knowledge and Value Invention in Logic Programming. Annals of Mathematics and Artificial Intelligence 50, 3–4, 333361.Google Scholar
Clark, K. L. 1978. Negation as Failure. In Logic and Data Bases, Gallaire, H. and Minker, J., Eds. Plenum.Google Scholar
Dal Palù, A., Dovier, A., Pontelli, E., and Rossi, G. 2009. GASP: Answer Set Programming with Lazy Grounding. Fundamenta Informaticae 96, 3, 297322.Google Scholar
Dovier, A., Formisano, A., and Pontelli, E. 2005. A Comparison of CLP(FD) and ASP Solutions to NP-Complete Problems. In International Conference on Logic Programming. Springer, 6782.Google Scholar
Gabbrielli, M. and Levi, G. 1991. Modeling Answer Constraints in Constraint Logic Programs. In Proc. Eighth Int'l Conf. on Logic Programming.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., and Thiele, S. 2008. A User's Guide to gringo, clasp, clingo, and iclingo.Google Scholar
Gelfond, M. and Lifschitz, V. 1988. The Stable Model Semantics for Logic Programming. In International Conference on Logic Programming 1988. 1070–1080.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical Negation in Logic Programs and Disjunctive Databases. New Generation Computing 9, 3/4, 365386.Google Scholar
Gupta, G., Bansal, A., Min, R., Simon, L., and Mallya, A. 2007. Coinductive Logic Programming and its Applications. Logic Programming, 27–44.Google Scholar
Hermenegildo, M. V., Bueno, F., Carro, M., López, P., Mera, E., Morales, J., and Puebla, G. 2012. An Overview of Ciao and its Design Philosophy. Theory and Practice of Logic Programming 12, 1–2 (January), 219–252. http://arxiv.org/abs/1102.5497.Google Scholar
Hölldobler, S. and Schweizer, L. 2014. Answer Set Programming and clasp, a Tutorial. In Young Scientists' International Workshop on Trends in Information Processing (YSIP). 77.Google Scholar
Holzbaur, C. 1995. OFAI CLP(Q,R) Manual, Edition 1.3.3. Tech. Rep. TR-95-09, Austrian Research Institute for Artificial Intelligence, Vienna.Google Scholar
Jaffar, J. and Maher, M. 1994. Constraint Logic Programming: A Survey. Journal of Logic Programming 19/20, 503581.Google Scholar
Janhunen, T., Kaminski, R., Ostrowski, M., Schellhorn, S., Wanko, P., and Schaub, T. 2017. Clingo goes Linear Constraints over Reals and Integers. TPLP 17, 5–6, 872888.Google Scholar
Ji, J., Wan, H., Wang, K., Wang, Z., Zhang, C., and Xu, J. 2016. Eliminating Disjunctions in Answer Set Programming by Restricted Unfolding. In IJCAI. 1130–1137.Google Scholar
Marple, K., Salazar, E., Chen, Z., and Gupta, G. 2017a. The s(ASP) Predicate Answer Set Programming System. The Association for Logic Programming Newsletter.Google Scholar
Marple, K., Salazar, E., and Gupta, G. 2017b. Computing Stable Models of Normal Logic Programs Without Grounding. CoRR abs/1709.00501.Google Scholar
Revesz, P. Z. 1993. A Closed-Form Evaluation for Datalog Queries with Integer (Gap)-Order Constraints. Theoretical Computer Science 116, 1, 117149.Google Scholar
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