Hostname: page-component-7bb8b95d7b-wpx69 Total loading time: 0 Render date: 2024-09-18T01:10:38.857Z Has data issue: false hasContentIssue false
  • English
  • Français

Efficiently Coupling the I-DLV Grounder with ASP Solvers

Published online by Cambridge University Press:  04 December 2018

FRANCESCO CALIMERI Open the ORCID record for FRANCESCO CALIMERI [Opens in a new window] ,
CARMINE DODARO Open the ORCID record for CARMINE DODARO [Opens in a new window] ,
DAVIDE FUSCÀ ,
SIMONA PERRI Open the ORCID record for SIMONA PERRI [Opens in a new window]  and
JESSICA ZANGARI
FRANCESCO CALIMERI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: calimeri@mat.unical.it, fusca@mat.unical.it, perri@mat.unical.it, zangari@mat.unical.it)
CARMINE DODARO*
Affiliation:
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Italy(e-mails: dodaro@dibris.unige.it)
DAVIDE FUSCÀ
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: calimeri@mat.unical.it, fusca@mat.unical.it, perri@mat.unical.it, zangari@mat.unical.it)
SIMONA PERRI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: calimeri@mat.unical.it, fusca@mat.unical.it, perri@mat.unical.it, zangari@mat.unical.it)
JESSICA ZANGARI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy (e-mails: calimeri@mat.unical.it, fusca@mat.unical.it, perri@mat.unical.it, zangari@mat.unical.it)
Get access Rights & Permissions [Opens in a new window]

Abstract

We present ${{{{$\mathscr{I}$}-}\textsc{dlv}}+{{$\mathscr{MS}$}}}$, a new answer set programming (ASP) system that integrates an efficient grounder, namely ${{{$\mathscr{I}$}-}\textsc{dlv}}$, with an automatic selector that inductively chooses a solver: depending on some inherent features of the instantiation produced by ${{{$\mathscr{I}$}-}\textsc{dlv}}$, machine learning techniques guide the selection of the most appropriate solver. The system participated in the latest (7th) ASP competition, winning the regular track, category SP (i.e., one processor allowed).

Keywords

knowledge representation and nonmonotonic reasoningconstraintstechnical notes and rapid communications
Type
Technical Note
Information
Theory and Practice of Logic Programming , Volume 20 , Issue 2 , March 2020 , pp. 205 - 224
Copyright
© Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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 International Conference on Logic Programming and Nonmonotonic Reasoning. Lecture Notes in Computer Science, vol. 10377, Espoo, Finland, 3–6 Jul. 2017, Balduccini, M. and Janhunen, T., Eds. Springer, 215–221.CrossRefGoogle Scholar
Alviano, M. and Dodaro, C. 2016. Anytime answer set optimization via unsatisfiable core shrinking. Theory and Practice of Logic Programming 16, 5–6, 533–551.CrossRefGoogle Scholar
Alviano, M., Dodaro, C., Leone, N. and Ricca, F. 2015. Advances in WASP. In International Conference on Logic Programming and Nonmonotonic Reasoning. Lecture Notes in Computer Science, vol. 9345, Lexington, KY, USA, 27–30 Sept. 2015, Calimeri, F., Ianni, G. and Truszczynski, M., Eds. Springer, 40–54.CrossRefGoogle Scholar
Alviano, M., Dodaro, C., Marques-Silva, J. and Ricca, F. 2015. Optimum stable model search: Algorithms and implementation. Journal of Logic and Computation.Google Scholar
Breiman, L. 2001. Random forests. Machine Learning 45, 1, 5–32.CrossRefGoogle Scholar
Brewka, G., Eiter, T. and Truszczynski, 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., Pooter, S. D., 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., Faber, W.,Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Ricca, F. and Schaub, T. 2012. Asp-core-2: Input language format. ASP Standardization Working Group. Technical Report.Google Scholar
Calimeri, F., Fuscà, D., Perri, S. and Zangari, J. since 2016. ${{{$\mathscr{I}$}-}\textsc{dlv}}$ homepage. URL: https://github.com/DeMaCS-UNICAL/I-DLV/wiki. [Accessed on March 20, 2018].Google Scholar
Calimeri, F., Fuscà, D., Perri, S. and Zangari, J. since 2017. ${{{{$\mathscr{I}$}-}\textsc{dlv}}+{{$\mathscr{MS}$}}}$ homepage. URL: https://github.com/DeMaCS-UNICAL/IDLV-MS. [Accessed on March 20, 2018].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 International Symposium on Practical Aspects of Declarative Languages. Lecture Notes in Computer Science, vol. 10702, Los Angeles, CA, USA, 8–9 Jan. 2018, Calimeri, F., Hamlen, K. W. and Leone, N., Eds. 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
Dal Palù, A., Dovier, A., Pontelli, E. and Rossi, G. 2009. GASP: Answer set programming with lazy grounding. Fundamenta Informaticae 96, 3, 297–322.CrossRefGoogle Scholar
Dantsin, E., Eiter, T., Gottlob, G. and Voronkov, A. 2001. Complexity and expressive power of logic programming. ACM Computing Survey 33, 3, 374–425.CrossRefGoogle Scholar
Dao-Tran, M., Eiter, T., Fink, M., Weidinger, G. and Weinzierl, A. 2012. Omiga: An open minded grounding on-the-fly answer set solver. In European Conference on Logics in Artificial Intelligence. Lecture Notes in Computer Science, vol. 7519, Toulouse, France, 26–28 Sept. 2012, del Cerro, L. F., Herzig, A. and Mengin, J., Eds. Springer, 480–483.Google Scholar
Dodaro, C., Alviano, M., Faber, W., Leone, N., Ricca, F. and Sirianni, M. 2011. The birth of a WASP: Preliminary report on a new ASP solver. In Italian Conference on Computational Logic. CEUR Workshop Proceedings, vol. 810, Pescara, Italy, Aug. 31 - Sept. 2, Fioravanti, F., Ed. CEUR-WS.org, 99–113.Google Scholar
Dodaro, C., Gasteiger, P., Leone, N., Musitsch, B., Ricca, F. and Schekotihin, K. 2016. Combining answer set programming and domain heuristics for solving hard industrial problems (application paper). Theory and Practice of Logic Programming 16, 5–6, 653–669.CrossRefGoogle Scholar
Dodaro, C., Leone, N., Nardi, B. and Ricca, F. 2015. Allotment problem in travel industry: A solution based on ASP. In International Conference on Web Reasoning and Rule Systems. Lecture Notes in Computer Science, vol. 9209, Berlin, Germany, 4–5 Aug. 2015, Cate, B. T. and Mileo, A., Eds. Springer, 77–92.CrossRefGoogle Scholar
Erdem, E., Gelfond, M. and Leone, N. 2016. Applications of answer set programming. AI Magazine 37, 3, 53–68.CrossRefGoogle Scholar
Esfahani, M. S. and Dougherty, E. R. 2014. Effect of separate sampling on classification accuracy. Bioinformatics 30, 2, 242–250.CrossRefGoogle Scholar
Faber, W., Leone, N. and Perri, S. 2012. The intelligent grounder of DLV. In Correct Reasoning–Essays on Logic-Based AI in Honour of Vladimir Lifschitz. Lecture Notes in Computer Science, vol. 7265. Springer, 247–264.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T. and Wanko, P. 2016. Theory solving made easy with clingo 5. In Technical Communications of the International Conference on Logic Programming. OASICS, vol. 52, New York, NY, USA, 16–21 Oct. 2016, Carro, M., King, A., Saeedloei, N. and De Vos, M., Eds. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2:1–2:15.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Romero, J. and Schaub, T. 2015. Progress in clasp series 3. In International Conference on Logic Programming and Nonmonotonic Reasoning. Lecture Notes in Computer Science, Lexington, KY, USA, 27–30 Sept. 2015, Calimeri, F., Ianni, G. and Truszczynski, M., Eds. vol. 9345. Springer, 368–383.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., König, A. and Schaub, T. 2011. Advances in gringo series 3. In International Conference on Logic Programming and Nonmonotonic Reasoning. Lecture Notes in Computer Science, vol. 6645, Vancouver, Canada, 16–19 May 2011, Delgrande, J. P. and Faber, W., Eds. Springer, 345–351.CrossRefGoogle Scholar
Gebser, M., Kaufmann, B. and Schaub, T. 2012. Conflict-driven answer set solving: From theory to practice. Artificial Intelligence 187, 52–89.CrossRefGoogle Scholar
Gebser, M., Maratea, M. and Ricca, F. 2016. What’s hot in the answer set programming competition. In AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, 12–17 Feb. 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 International Conference on Logic Programming and Nonmonotonic Reasoning. Lecture Notes in Computer Science, vol. 10377, Espoo, Finland, 3–6 Jul. 2017, Balduccini, M. and Janhunen, T., Eds. Springer, 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
Gebser, M., Schaub, T. and Thiele, S. 2007. Gringo: A new grounder for answer set programming. In International Conference on Logic Programming and Nonmonotonic Reasoning. Lecture Notes in Computer Science, vol. 4483, Tempe, AZ, USA, May 15–17 2007, Baral, C., Brewka, G. and Schlipf, J. S., Eds. Springer, 266–271.CrossRefGoogle Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming In International Conference and Symposium on Logic Programming, Seattle, WA, USA, Kowalski, R. A. and Bowen, K. A., Eds. MIT Press, 1070–1080.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 3/4, 365–386.CrossRefGoogle Scholar
Grasso, G., Iiritano, S., Leone, N. and Ricca, F. 2009. Some DLV applications for knowledge management. In International Conference on Logic Programming and Nonmonotonic Reasoning. Lecture Notes in Computer Science, vol. 5753, Potsdam, Germany, 14–18 Sept. 2009. Erdem, E., Lin, F. and Schaub, T., Eds. Springer, 591–597.CrossRefGoogle Scholar
Hoos, H., Lindauer, M. T. and Schaub, T. 2014. claspfolio 2: Advances in algorithm selection for answer set programming. Theory and Practice of Logic Programming 14, 4–5, 569–585.CrossRefGoogle Scholar
Kaufmann, B., Leone, N., Perri, S. and Schaub, T. 2016. Grounding and solving in answer set programming. AI Magazine 37, 3, 25–32.CrossRefGoogle Scholar
Kotthoff, L., Thornton, C., Hoos, H. H., Hutter, F. and Leyton-Brown, K. 2017. Auto-weka 2.0: Automatic model selection and hyperparameter optimization in WEKA. Journal of Machine Learning Research 18, 25:1-25:5.Google 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 Transaction on Computational Logic 7, 3, 499–562.CrossRefGoogle Scholar
Leone, N. and Ricca, F. 2015. Answer set programming: A tour from the basics to advanced development tools and industrial applications. In International Summer School on Reasoning Web. Lecture Notes in Computer Science, vol. 9203, Berlin, Germany, Jul. 31-Aug. 4 2015, Faber, W. and Paschke, A., Eds. Springer, 308–326.Google Scholar
Lierler, Y. and Maratea, M. 2004. Cmodels-2: Sat-based answer set solver enhanced to non-tight programs. In International Conference on Logic Programming and Nonmonotonic Reasoning. Lecture Notes in Computer Science, vol. 2923, Fort Lauderdale, FL, USA, 6–8 Jan. 2004, Lifschitz, V. and Niemelä, I., Eds. Springer, 346–350.CrossRefGoogle 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., 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 63, 1–3, 70–89.CrossRefGoogle Scholar
Nogueira, M., Balduccini, M., Gelfond, M., Watson, R. and Barry, M. 2001. An a-prolog decision support system for the space shuttle. In International Symposium on Practical Aspects of Declarative Languages. Lecture Notes in Computer Science, vol. 1990, Las Vegas, NV, USA, 11–12 Mar. 2001, Ramakrishnan, I. V., Eds. Springer, 169–183.CrossRefGoogle Scholar
Pulina, L. and Tacchella, A. 2009. A self-adaptive multi-engine solver for quantified boolean formulas. Constraints 14, 1, 80–116.CrossRefGoogle Scholar
Ricca, F., Grasso, G., Alviano, M., Manna, M., Lio, V., Iiritano, S. and Leone, N. 2012. Team-building with answer set programming in the gioia-tauro seaport. Theory and Practice of Logic Programming 12, 3, 361–381.CrossRefGoogle Scholar
Rice, J. R. 1976. The algorithm selection problem. Advances in Computers 15, 65–118.CrossRefGoogle Scholar
Simons, P., Niemelä, I. and Soininen, T. 2002. Extending and implementing the stable model semantics. Artificial Intelligence 138, 1–2, 181–234.CrossRefGoogle Scholar
Smola, A. J. and Schölkopf, B. 2004. A tutorial on support vector regression. Statistics and Computing 14, 3, 199–222.CrossRefGoogle Scholar
Syrjänen, T. 2001. Omega-restricted logic programs. In International Conference on Logic Programming and Nonmonotonic Reasoning. Lecture Notes in Computer Science, vol. 2173, Vienna, Austria, 17–19 Sept. 2001, Eiter, T., Faber, W. and Truszczynski, M., Eds. Springer, 267–279.Google Scholar
Syrjänen, T. 2002. Lparse 1.0 user’s manual. URL: http://www.tcs.hut.fi/Software/smodels/lparse.ps.gz. [Accessed on March 20, 2018].Google Scholar
Tiihonen, J., Soininen, T., Niemelä, I. and Sulonen, R. 2003. A practical tool for mass-customising configurable products. In International Conference on Engineering Design, Stockholm, Sweden, 19–21 Aug. 2003, Folkeson, A., Gralen, K., Norell, M. and Sellgren, U., Eds.Google Scholar
Ullman, J. D. 1988. Principles of Database and Knowledge-Base Systems, vol. I. Computer Science Press, New York, NY, USA.Google Scholar
Weinzierl, A. 2017. Blending lazy-grounding and CDNL search for answer-set solving. In International Conference on Logic Programming and Nonmonotonic Reasoning. Lecture Notes in Computer Science, vol. 10377, Espoo, Finland, 3–6 Jul. 2017, Balduccini, M. and Janhunen, T., Eds. Springer, 191–204.CrossRefGoogle Scholar
Xu, L., Hutter, F., Hoos, H. H. and Leyton-Brown, K. 2008. Satzilla: Portfolio-based algorithm selection for SAT. Journal of Artificial Intelligence Research 32, 565–606.CrossRefGoogle Scholar