Hostname: page-component-7b7567c8c-fvjsw Total loading time: 0 Render date: 2026-11-14T21:42:12.817Z Has data issue: false hasContentIssue false
  • English
  • Français

SUNNY: a Lazy Portfolio Approach for Constraint Solving

Published online by Cambridge University Press:  21 July 2014

ROBERTO AMADINI,
MAURIZIO GABBRIELLI and
JACOPO MAURO
ROBERTO AMADINI
Affiliation:
Department of Computer Science and Engineering/Lab. Focus INRIA, University of Bologna, Italy.
MAURIZIO GABBRIELLI
Affiliation:
Department of Computer Science and Engineering/Lab. Focus INRIA, University of Bologna, Italy.
JACOPO MAURO
Affiliation:
Department of Computer Science and Engineering/Lab. Focus INRIA, University of Bologna, Italy.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Within the context of constraint solving, a portfolio approach allows one to exploit the synergy between different solvers in order to create a globally better solver. In this paper we present SUNNY: a simple and flexible algorithm that takes advantage of a portfolio of constraint solvers in order to compute — without learning an explicit model — a schedule of them for solving a given Constraint Satisfaction Problem (CSP). Motivated by the performance reached by SUNNY vs. different simulations of other state of the art approaches, we developed sunny-csp, an effective portfolio solver that exploits the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests conducted on exhaustive benchmarks of MiniZinc models show that the actual performance of sunny-csp conforms to the predictions. This is encouraging both for improving the power of CSP portfolio solvers and for trying to export them to fields such as Answer Set Programming and Constraint Logic Programming.

Keywords

Algorithms PortfolioArtificial IntelligenceConstraint SatisfactionMachine Learning

Information

Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 14 , Special Issue 4-5: 30th International Conference on Logic Programming , July 2014, pp. 509 - 524
Copyright
Copyright © Cambridge University Press 2014 

References

Amadini, R., Gabbrielli, M., and Mauro, J. 2013a. An Empirical Evaluation of Portfolios Approaches for Solving CSPs. In CPAIOR. Lecture Notes in Computer Science, vol. 7874. Springer.Google Scholar
Amadini, R., Gabbrielli, M., and Mauro, J. 2013b. Features for Building CSP Portfolio Solvers. CoRR abs/1308.0227.Google Scholar
Amadini, R., Gabbrielli, M., and Mauro, J. 2014a. An Enhanced Features Extractor for a Portfolio of Constraint Solvers. In SAC.CrossRefGoogle Scholar
Amadini, R., Gabbrielli, M., and Mauro, J. 2014b. Portfolio Approaches for Constraint Optimization Problems. In LION.CrossRefGoogle Scholar
Arlot, S. and Celisse, A. 2010. A survey of cross-validation procedures for model selection. Statistics Surveys 4, 40–79.CrossRefGoogle Scholar
Balduccini, M. 2011. Learning and using domain-specific heuristics in asp solvers. AI Commun. 24, 2, 147–164.CrossRefGoogle Scholar
Cipriano, R., Dovier, A., and Mauro, J. 2008. Compiling and executing declarative modeling languages to gecode. In ICLP. Lecture Notes in Computer Science, vol. 5366. Springer, 744–748.Google Scholar
CSP Competition 2009. Third International CSP Solver Competition 2008. http://www.cril.univ-artois.fr/CPAI09/ Google Scholar
de Cat, B., Bogaerts, B., Devriendt, J., and Denecker, M. 2013. Model expansion in the presence of function symbols using constraint programming. In ICTAI. 1068–1075.CrossRefGoogle Scholar
Gebruers, C., Guerri, A., Hnich, B., and Milano, M. 2004. Making Choices Using Structure at the Instance Level within a Case Based Reasoning Framework. In CPAIOR. Lecture Notes in Computer Science, vol. 3011. Springer, 380–386.Google 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 LPNMR. Lecture Notes in Computer Science, vol. 6645. Springer, 352–357.Google Scholar
Gomes, C. P. and Selman, B. 2001. Algorithm portfolios. Artif. Intell. 126, 1–2, 43–62.CrossRefGoogle Scholar
Hoos, H., Kaminski, R., Schaub, T., and Schneider, M. T. 2012. aspeed: ASP-based Solver Scheduling. In ICLP (Technical Communications). 176–187.Google Scholar
Hutter, F., Xu, L., Hoos, H. H., and Leyton-Brown, K. 2012. Algorithm Runtime Prediction: The State of the Art. CoRR abs/1211.0906.Google Scholar
Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H., and Sellmann, M. 2011. Algorithm Selection and Scheduling. In CP. Lecture Notes in Computer Science, vol. 6876. Springer.Google Scholar
Kadioglu, S., Malitsky, Y., Sellmann, M., and Tierney, K. 2010. ISAC - Instance-Specific Algorithm Configuration. In ECAI. Frontiers in Artificial Intelligence and Applications, vol. 215. IOS Press.Google Scholar
Kotthoff, L. 2012. Algorithm Selection for Combinatorial Search Problems: A Survey. CoRR abs/1210.7959.Google Scholar
Mackworth, A. K. 1977. Consistency in Networks of Relations. Artif. Intell. 8, 1, 99–118.CrossRefGoogle Scholar
Malitsky, Y., Sabharwal, A., Samulowitz, H., and Sellmann, M. 2013. Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering. In IJCAI. IJCAI/AAAI.Google Scholar
Malitsky, Y. and Sellmann, M. 2012. Instance-Specific Algorithm Configuration as a Method for Non-Model-Based Portfolio Generation. In CPAIOR. Lecture Notes in Computer Science, vol. 7298. Springer.Google Scholar
Maratea, M., Pulina, L., and Ricca, F. 2012. The Multi-Engine ASP Solver me-asp. In JELIA. Lecture Notes in Computer Science, vol. 7519. Springer, 484–487.Google Scholar
Nikolic, M., Maric, F., and Janicic, P. 2009. Instance-Based Selection of Policies for SAT Solvers. In SAT. Lecture Notes in Computer Science, vol. 5584. Springer, 326–340.Google Scholar
OMahony, E., Hebrard, E., Holland, A., Nugent, C., and OSullivan, B. 2009. Using case-based reasoning in an algorithm portfolio for constraint solving. AICS 08.Google Scholar
Pulina, L. and Tacchella, A. 2007. A Multi-engine Solver for Quantified Boolean Formulas. In CP. Lecture Notes in Computer Science, vol. 4741. Springer, 574–589.Google Scholar
Pulina, L. and Tacchella, A. 2009. A self-adaptive multi-engine solver for quantified boolean formulas. Constraints 14, 1, 80–116.CrossRefGoogle Scholar
Rice, J. R. 1976. The Algorithm Selection Problem. Advances in Computers 15, 65–118.CrossRefGoogle Scholar
Samulowitz, H., Reddy, C., Sabharwal, A., and Sellmann, M. 2013. Snappy: A simple algorithm portfolio. In SAT. Lecture Notes in Computer Science, vol. 7962. Springer, 422–428.Google Scholar
SAT Competition 2013. SAT Competition 2013. http://satcompetition.org/2013/ Google Scholar
Smith-Miles, K. 2008. Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41, 1.Google Scholar
Stern, D. H., Samulowitz, H., Herbrich, R., Graepel, T., Pulina, L., and Tacchella, A. 2010. Collaborative expert portfolio management. In AAAI. AAAI Press.Google Scholar
Wilson, D., Leake, D., and Bramley, R. 2000. Case-Based Recommender Components for Scientific Problem-Solving Environments. In In Procs. of the 16th International Association for Mathematics and Computers in Simulation World Congress.Google Scholar
Xu, L., Hutter, F., Hoos, H. H., and Leyton-Brown, K. 2007. SATzilla-07: The Design and Analysis of an Algorithm Portfolio for SAT. In CP. Lecture Notes in Computer Science, vol. 4741. Springer.Google Scholar
Xu, L., Hutter, F., Shen, J., Hoos, H., and Leyton-Brown, K. 2012. SATzilla2012: Improved algorithm selection based on cost-sensitive classification models. Solver description, SAT Challenge 2012.Google Scholar