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Compiling Input* FO(·) inductive definitions into tabled prolog rules for IDP3

Published online by Cambridge University Press:  25 September 2013

JOACHIM JANSEN ,
ALBERT JORISSEN  and
GERDA JANSSENS
JOACHIM JANSEN
Affiliation:
Department of Computer Science, KU Leuven (e-mail: firstname.secondname@cs.kuleuven.be, albert.jorissen@ulyssis.org)
ALBERT JORISSEN
Affiliation:
Department of Computer Science, KU Leuven (e-mail: firstname.secondname@cs.kuleuven.be, albert.jorissen@ulyssis.org)
GERDA JANSSENS
Affiliation:
Department of Computer Science, KU Leuven (e-mail: firstname.secondname@cs.kuleuven.be, albert.jorissen@ulyssis.org)
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Abstract

FO(·)IDP3 extends first-order logic with inductive definitions, partial functions, types and aggregates. Its model generator IDP3 first grounds the theory and then uses search to find the models. The grounder uses Lifted Unit Propagation (LUP) to reduce the size of the groundings of problem specifications in IDP3. LUP is in general very effective, but performs poorly on definitions of predicates whose two-valued interpretation can be computed from data in the input structure. To solve this problem, a preprocessing step is introduced that converts such definitions to Prolog code and uses XSB Prolog to compute their interpretation. The interpretation of these predicates is then added to the input structure, their definitions are removed from the theory and further processing is done by the standard IDP3 system. Experimental results show the effectiveness of our method.

Keywords

program transformationFO(·)logic programmingtablingknowledge base systemsIDP systemdeclarative modeling
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 13 , Special Issue 4-5: 29th International Conference on Logic Programming , July 2013 , pp. 691 - 704
Copyright
Copyright © 2013 [JOACHIM JANSEN, ALBERT JORISSEN and GERDA JANSSENS] 

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References

Aavani, A., Wu, X. N., Tasharrofi, S., Ternovska, E. and Mitchell, D. G. 2012. Enfragmo: A system for modelling and solving search problems with logic. In LPAR, 15–22.CrossRefGoogle Scholar
Alviano, M. and Faber, W. 2011. Dynamic magic sets and super-coherent answer set programs. AI Communications 24, 2, 125–145.CrossRefGoogle Scholar
Blockeel, H., Bogaerts, B., Bruynooghe, M., De Cat, B., De Pooter, S., Denecker, M., Labarre, A., Ramon, J. and Verwer, S. 2012. Modeling machine learning and data mining problems with FO(⋅). In Proceedings of the 28th International Conference on Logic Programming - Technical Communications (ICLP'12), Dovier, A. and Costa, V. Santos, Eds. Schloss Daghstuhl - Leibniz-Zentrum fuer Informatik, 14–25.Google Scholar
De Pooter, S., Wittocx, J. and Denecker, M. 2011. A prototype of a knowledge-based programming environment. In International Conference on Applications of Declarative Programming and Knowledge Management.Google Scholar
Eén, N. and Sörensson, N. 2003. An extensible SAT-solver. In SAT, Giunchiglia, E. and Tacchella, A., Eds. LNCS, vol. 2919, Springer, 502–518.Google Scholar
Faber, W., Leone, N., Mateis, C. and Pfeifer, G. 1999. Using database optimization techniques for nonmonotonic reasoning. In INAP Organizing Committee DDLP'99, 135–139.Google Scholar
Faber, W., Leone, N. and Perri, S. 2012. The intelligent grounder of DLV. Correct Reasoning, 247–264.Google Scholar
Gebser, M., Kaminski, R., König, A. and Schaub, T. 2011. Advances in gringo series 3. In Proceedings of the Eleventh International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'11), Delgrande, J. and Faber, W., Eds. Lecture Notes in Artificial Intelligence, vol. 6645, Springer-Verlag, 345–351.CrossRefGoogle Scholar
Gebser, M., Schaub, T. and Thiele, S. 2007. GrinGo : A new grounder for answer set programming. In LPNMR, Baral, C., Brewka, G., and Schlipf, J. S., Eds. LNCS, vol. 4483, Springer, 266–271.Google Scholar
Leone, N., Perri, S. and Scarcello, F. 2001. Improving ASP instantiators by join-ordering methods. In Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR '01), Springer-Verlag, London, UK, 280–294.Google Scholar
Mariën, M., Wittocx, J., Denecker, M. and Bruynooghe, M. 2008. SAT(ID): Satisfiability of propositional logic extended with inductive definitions. In SAT, Büning, H. Kleine and Zhao, X., Eds. LNCS, vol. 4996, Springer, 211–224.Google Scholar
Mitchell, D. G. and Ternovska, E. 2005. A framework for representing and solving NP search problems. In AAAI, Veloso, M. M. and Kambhampati, S., Eds. AAAI Press/The MIT Press, 430–435.Google Scholar
Swift, T. and Warren, D. S. 2012. XSB: Extending prolog with tabled logic programming. Theory and Practice of Logic Programming 12, 157–187.10.1017/S1471068411000500CrossRefGoogle Scholar
Swift, T., Warren, D. S., Sagonas, K., Freire, J., Rao, P., Cui, B., Johnson, E., de Castro, L., Marques, R. F., Saha, D., Dawson, S. and Kifer, M. 2013. The XSB System Version 3.3.x Volume 1: Programmer's Manual.Google Scholar
Syrjänen, T. 1998. Implementation of local grounding for logic programs with stable model semantics. Tech. Rep. B18, Helsinki University of Technology, Finland.Google Scholar
Vaezipoor, P., Mitchell, D. G. and Mariën, M. 2011. Lifted unit propagation for effective grounding. CoRR abs/1109.1317.Google Scholar
Vlaeminck, H. 2012. Applications of Feasible Inference for Expressive Logics. PhD thesis, Department of Computer Science, K.U.Leuven, Leuven, Belgium.Google Scholar
Wittocx, J. 2010. Finite Domain and Symbolic Inference Methods for Extensions of First-Order Logic. PhD thesis, Department of Computer Science, K.U.Leuven, Leuven, Belgium.Google Scholar
Wittocx, J., Denecker, M. and Bruynooghe, M. 2013. Constraint propagation for first-order logic and inductive definitions. ACM Transactions on Computational Logic. Accepted.10.1145/2499937.2499938CrossRefGoogle Scholar
Wittocx, J., Mariën, M. and Denecker, M. 2010. Grounding FO and FO(ID) with bounds. Journal of Artificial Intelligence Research 38, 223–269.CrossRefGoogle Scholar