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The Seventh Answer Set Programming Competition: Design and Results

Published online by Cambridge University Press:  31 May 2019

MARTIN GEBSER Open the ORCID record for MARTIN GEBSER [Opens in a new window] ,
MARCO MARATEA  and
FRANCESCO RICCA Open the ORCID record for FRANCESCO RICCA [Opens in a new window]
MARTIN GEBSER
Affiliation:
Institute for Computer Science, University of Potsdam, Potsdam, Germany (e-mail: [email protected])
MARCO MARATEA*
Affiliation:
DIBRIS, University of Genova, Genova, Italy (e-mail: [email protected])
FRANCESCO RICCA
Affiliation:
Dipartimento di Matematica e Informatica, Universitá della Calabria, Rende, Italy (e-mail: [email protected])
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Abstract

Answer Set Programming (ASP) is a prominent knowledge representation language with roots in logic programming and non-monotonic reasoning. Biennial ASP competitions are organized in order to furnish challenging benchmark collections and assess the advancement of the state of the art in ASP solving. In this paper, we report on the design and results of the Seventh ASP Competition, jointly organized by the University of Calabria (Italy), the University of Genova (Italy), and the University of Potsdam (Germany), in affiliation with the 14th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2017).

Keywords

Answer Set Programmingcompetition
Type
Technical Note
Information
Theory and Practice of Logic Programming , Volume 20 , Issue 2 , March 2020 , pp. 176 - 204
Copyright
© Cambridge University Press 2019 

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References

Alviano, M., Calimeri, F., Dodaro, C., Fuscà, D., Leone, N., Perri, S., Ricca, F., Veltri, P. and Zangari, J. 2017. The ASP system DLV2. In Proceedings of the Fourteenth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’17), Balduccini, M. and Janhunen, T., Eds. Lecture Notes in AI (LNAI), vol. 10377. Springer-Verlag, 215–221.CrossRefGoogle Scholar
Alviano, M., Dodaro, C., Leone, N. and Ricca, F. 2015. Advances in WASP. In Proceedings of the Thirteenth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’15), Calimeri, F., Ianni, G. and Truszczyński, M., Eds. Lecture Notes in Computer Science, vol. 9345. Springer-Verlag, 40–54.CrossRefGoogle Scholar
Amendola, G., Dodaro, C., Faber, W., Leone, N. and Ricca, F. 2017. On the computation of paracoherent answer sets. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI’17), Singh, S. P. and Markovitch, S., Eds. AAAI Press, 1034–1040.Google Scholar
Amendola, G., Dodaro, C., Faber, W. and Ricca, F. 2018. Externally supported models for efficient computation of paracoherent answer sets. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI’18), McIlraith, S. A. and Weinberger, K. Q., Eds. AAAI Press, 1720–1727.Google Scholar
Amendola, G., Eiter, T., Fink, M., Leone, N. and Moura, J. 2016. Semi-equilibrium models for paracoherent answer set programs. Artificial Intelligence 234, 219–271.Google Scholar
Amendola, G., Ricca, F. and Truszczyński, M. 2017. Generating hard random Boolean formulas and disjunctive logic programs. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI’17), Sierra, C., Ed. ijcai.org, 532–538.CrossRefGoogle Scholar
Applegate, D., Bixby, R., Chvátal, V. and Cook, W. 2007. The Traveling Salesman Problem: A Computational Study. Princeton University Press.Google Scholar
Balduccini, M., Magazzeni, D., Maratea, M. and Leblanc, E. 2017. CASP solutions for planning in hybrid domains. Theory and Practice of Logic Programming 17, 4, 591–633.CrossRefGoogle Scholar
Baral, C. 2003. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press.CrossRefGoogle Scholar
Ben-Eliyahu, R. and Dechter, R. 1994. Propositional semantics for disjunctive logic programs. Annals of Mathematics and Artificial Intelligence 12, 53–87.CrossRefGoogle Scholar
Bogaerts, B., Janhunen, T. and Tasharrofi, S. 2016. Stable-unstable semantics: Beyond NP with normal logic programs. Theory and Practice of Logic Programming 16, 5–6, 570–586.CrossRefGoogle Scholar
Bomanson, J., Gebser, M. and Janhunen, T. 2014. Improving the normalization of weight rules in answer set programs. In Proceedings of the Fourteenth European Conference on Logics in Artificial Intelligence (JELIA’14), Fermé, E. and Leite, J., Eds. Lecture Notes in Artificial Intelligence, vol. 8761. Springer-Verlag, 166–180.Google Scholar
Bomanson, J., Gebser, M. and Janhunen, T. 2016. Rewriting optimization statements in answer-set programs. In Technical Communications of the Thirty-Second International Conference on Logic Programming (ICLP’16), Carro, M. and King, A., Eds. Open Access Series in Informatics, vol. 52. Schloss Dagstuhl, 5:1–5:15.Google Scholar
Bomanson, J., Gebser, M., Janhunen, T., Kaufmann, B. and Schaub, T. 2016. Answer set programming modulo acyclicity. Fundamenta Informaticae 147, 1, 63–91.CrossRefGoogle Scholar
Brewka, G., Eiter, T. and Truszczyński, M. 2011. Answer set programming at a glance. Communications of the ACM 54, 12, 92–103.CrossRefGoogle Scholar
Bruynooghe, M., Blockeel, H., Bogaerts, B., De Cat, B., De Pooter, S., Jansen, J., Labarre, A., Ramon, J., Denecker, M. and Verwer, S. 2015. Predicate logic as a modeling language: Modeling and solving some machine learning and data mining problems with IDP3. Theory and Practice of Logic Programming 15, 6, 783–817.CrossRefGoogle Scholar
Calimeri, F., Dodaro, C., Fuscà, D., Perri, S. and Zangari, J. 2019. Efficiently coupling the I-DLV grounder with ASP solvers. Theory and Practice of Logic Programming. To appear.Google Scholar
Calimeri, F., Fuscà, D., Perri, S. and Zangari, J. 2016. I-DLV: The new intelligent grounder of DLV. In Proceedings of AI*IA 2016: Advances in Artificial Intelligence - Fifteenth International Conference of the Italian Association for Artificial Intelligence, Adorni, G., Cagnoni, S., Gori, M. and Maratea, M., Eds. Lecture Notes in Computer Science, vol. 10037. Springer, 192–207.Google Scholar
Calimeri, F., Fuscà, D., Perri, S. and Zangari, J. 2017. I-DLV: The new intelligent grounder of DLV. Intelligenza Artificiale 11, 1, 5–20.CrossRefGoogle Scholar
Calimeri, F., Fuscà, D., Perri, S. and Zangari, J. 2018. Optimizing answer set computation via heuristic-based decomposition. In Proceedings of the Twentieth International Symposium on Practical Aspects of Declarative Languages (PADL’18), Calimeri, F., Hamlen, K. W. and Leone, N., Eds. Lecture Notes in Computer Science, vol. 10702. Springer, 135–151.CrossRefGoogle Scholar
Calimeri, F., Gebser, M., Maratea, M. and Ricca, F. 2016. Design and results of the fifth answer set programming competition. Artificial Intelligence 231, 151–181.CrossRefGoogle Scholar
Calimeri, F., Ianni, G. and Ricca, F. 2014. The third open answer set programming competition. Theory and Practice of Logic Programming 14, 1, 117–135.CrossRefGoogle Scholar
Cussens, J. 2011. Bayesian network learning with cutting planes. In Proceedings of the Twenty-Seventh International Conference on Uncertainty in Artificial Intelligence (UAI’11), Cozman, F. and Pfeffer, A., Eds. AUAI Press, 153–160.Google 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, 289–323.CrossRefGoogle Scholar
Eiter, T., Ianni, G. and Krennwallner, T. 2009. Answer set programming: A primer. In Reasoning Web. Semantic Technologies for Information Systems, 5th International Summer School - Tutorial Lectures. Brixen-Bressanone, Italy, 40–110.Google Scholar
Fages, F. 1994. Consistency of Clark’s completion and existence of stable models. Journal of Methods of Logic in Computer Science 1, 1, 51–60.Google Scholar
Fuscà, D., Calimeri, F., Zangari, J. and Perri, S. 2017. I-DLV+MS: Preliminary report on an automatic ASP solver selector. In Proceedings of the Twenty-Fourth RCRA International Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion (RCRA’17), Maratea, M. and Serina, I., Eds. CEUR Workshop Proceedings, vol. 2011. CEUR-WS.org, 26–32.Google Scholar
Gebser, M., Janhunen, T. and Rintanen, J. 2014. Answer set programming as SAT modulo acyclicity. In Proceedings of the Twenty-First European Conference on Artificial Intelligence (ECAI’14), Schaub, T., Friedrich, G. and O’Sullivan, B., Eds. Frontiers in Artificial Intelligence and Applications, vol. 263. IOS Press, 351–356.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T. and Thiele, S. 2008. Engineering an incremental ASP solver. In Proceedings of the Twenty-Fourth International Conference on Logic Programming (ICLP’08), Garcia de la Banda, M. and Pontelli, E., Eds. Lecture Notes in Computer Science, vol. 5366. Springer-Verlag, 190–205.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Romero, J. and Schaub, T. 2015. Progress in clasp series 3. In Proceedings of the Thirteenth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’15), Calimeri, F., Ianni, G. and Truszczyński, M., Eds. Lecture Notes in Computer Science, vol. 9345. Springer-Verlag, 368–383.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T., Schneider, M. T. and Ziller, S. 2011. A portfolio solver for answer set programming: Preliminary report. In Proceedings of the Eleventh International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’11). Lecture Notes in Computer Science, vol. 6645. Springer, Vancouver, Canada, 352–357.CrossRefGoogle Scholar
Gebser, M., Leone, N., Maratea, M., Perri, S., Ricca, F. and Schaub, T. 2018. Evaluation techniques and systems for answer set programming: a survey. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI 2018), Lang, J., Ed. ijcai.org, 5450–5456.CrossRefGoogle Scholar
Gebser, M., Maratea, M. and Ricca, F. 2016. What’s hot in the answer set programming competition. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016), Schuurmans, D. and Wellman, M. P., Eds. AAAI Press, 4327–4329.Google Scholar
Gebser, M., Maratea, M. and Ricca, F. 2017a. The design of the seventh answer set programming competition. In Proceedings of the Fourteenth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’17), Balduccini, M. and Janhunen, T., Eds. Lecture Notes in AI (LNAI), vol. 10377. Springer-Verlag, 3–9.CrossRefGoogle Scholar
Gebser, M., Maratea, M. and Ricca, F. 2017b. The sixth answer set programming competition. Journal of Artificial Intelligence Research 60, 41–95.CrossRefGoogle Scholar
Gelfond, M. and Leone, N. 2002. Logic programming and knowledge representation – the A-prolog perspective. Artificial Intelligence 138, 1–2, 3–38.CrossRefGoogle Scholar
Guerinik, N. and Caneghem, M. V. 1995. Solving crew scheduling problems by constraint programming. In Proceedings of the First International Conference on Principles and Practice of Constraint Programming (CP’95), Montanari, U. and Rossi, F., Eds. Lecture Notes in Computer Science, vol. 976. Springer, 481–498.Google Scholar
Havur, G., Cabanillas, C., Mendling, J. and Polleres, A. 2016. Resource allocation with dependencies in business process management systems. In Proceedings of the Business Process Management Forum (BPM’16), Rosa, M. L., Loos, P. and Pastor, O., Eds. Lecture Notes in Business Information Processing, vol. 260. Springer, 3–19.CrossRefGoogle Scholar
Inoue, K. and Sakama, C. 1996. A fixpoint characterization of abductive logic programs. Journal of Logic Programming 27, 2, 107–136.CrossRefGoogle Scholar
Janhunen, T., Gebser, M., Rintanen, J., Nyman, H., Pensar, J. and Corander, J. 2017. Learning discrete decomposable graphical models via constraint optimization. Statistics and Computing 27, 1, 115–130.CrossRefGoogle Scholar
Janhunen, T. and Niemelä, I. 2011. Compact translations of non-disjunctive answer set programs to propositional clauses. In Proceedings of the Symposium on Constructive Mathematics and Computer Science in Honour of Michael Gelfonds 65th Anniversary. Lecture Notes in Computer Science, vol. 6565. Springer, 111–130.Google Scholar
Koponen, L., Oikarinen, E., Janhunen, T. and Säilä, L. 2015. Optimizing phylogenetic supertrees using answer set programming. Theory and Practice of Logic Programming 15, 4–5, 604–619.CrossRefGoogle Scholar
Lefèvre, C., Béatrix, C., Stéphan, I. and Garcia, L. 2017. ASPeRiX, a first-order forward chaining approach for answer set computing. Theory and Practice of Logic Programming 17, 3, 266–310.CrossRefGoogle Scholar
Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S. and Scarcello, F. 2006. The DLV system for knowledge representation and reasoning. ACM Transactions on Computational Logic 7, 3, 499–562.CrossRefGoogle Scholar
Lierler, Y., Maratea, M. and Ricca, F. 2016. Systems, engineering environments, and competitions. AI Magazine 37, 3, 45–52.CrossRefGoogle Scholar
Lifschitz, V. 2002. Answer set programming and plan generation. Artificial Intelligence 138, 39–54.CrossRefGoogle Scholar
Lifschitz, V. 2008. Twelve definitions of a stable model. In Proceedings of the Twenty-Fourth International Conference on Logic Programming (ICLP’08), Garcia de la Banda, M. and Pontelli, E., Eds. Lecture Notes in Computer Science, vol. 5366. Springer-Verlag, 37–51.Google Scholar
Liu, G., Janhunen, T. and Niemelä, I. 2012. Answer set programming via mixed integer programming. In Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning (KR’12), Brewka, G., Eiter, T. and McIlraith, S. A., Eds. AAAI Press, 32–42.Google Scholar
Maratea, M., Pulina, L. and Ricca, F. 2012. The multi-engine ASP solver me-asp. In Proceedings of the 13th European Conference on Logics in Artificial Intelligence (JELIA 2012), del Cerro, L. F., Herzig, A. and Mengin, J., Eds. Lecture Notes in Computer Science, vol. 7519. Springer, 484–487.CrossRefGoogle Scholar
Maratea, M., Pulina, L. and Ricca, F. 2013. Automated selection of grounding algorithm in answer set programming. In Advances in Artificial Intelligence - Proceedings of the 13th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2013), Baldoni, M., Baroglio, C., Boella, G. and Micalizio, R., Eds. Lecture Notes in Computer Science, vol. 8249. Springer, 73–84.Google Scholar
Maratea, M., Pulina, L. and Ricca, F. 2014. A multi-engine approach to answer-set programming. Theory and Practice of Logic Programming 14, 6, 841–868.CrossRefGoogle Scholar
Maratea, M., Pulina, L. and Ricca, F. 2015. Multi-level algorithm selection for ASP. In Proceedings of the Thirteenth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’15), Calimeri, F., Ianni, G. and Truszczyński, M., Eds. Lecture Notes in Computer Science, vol. 9345. Springer-Verlag, 439–445.CrossRefGoogle Scholar
Maratea, M., Ricca, F., Faber, W. and Leone, N. 2008. Look-back techniques and heuristics in DLV: Implementation, evaluation and comparison to QBF solvers. Journal of Algorithms in Cognition, Informatics and Logics 63, 1–3, 70–89.Google Scholar
Marek, V. W. and Truszczyński, M. 1999. Stable models and an alternative logic programming paradigm. In The Logic Programming Paradigm – A 25-Year Perspective, Apt, K. R., Marek, V. W., Truszczyński, M. and Warren, D. S., Eds. Springer-Verlag, 375–398.CrossRefGoogle Scholar
Marple, K. and Gupta, G. 2014. Dynamic consistency checking in goal-directed answer set programming. Theory and Practice of Logic Programming 14, 4–5, 415–427.CrossRefGoogle Scholar
Mellarkod, V., Gelfond, M. and Zhang, Y. 2008. Integrating answer set programming and constraint logic programming. Annals of Mathematics and Artificial Intelligence 53, 1–4, 251–287.CrossRefGoogle Scholar
Niemelä, I. 1999. Logic programming with stable model semantics as constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25, 3–4, 241–273.CrossRefGoogle Scholar