Abstract
Given the breadth of constraint satisfaction problems (CSPs) and the wide variety of CSP solvers, it can be difficult to determine a priori which solving method is best suited to a problem. We explore the use of machine learning to predict which solving method will be most effective for a given problem. Our investigation studies the problem of attribute selection for CSPs, and supervised learning to classify CSP instances drawn from four distinct CSP classes. We limit our study to the choice of two well-known, but simple, CSP solvers. We show that the average performance of the resulting solver is very close to the average performance of a CSP solver based on an oracle.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Régin, J.: A filtering algorithm for constraints of difference in csps. In: Proceedings of the National Conference on Artificial Intelligence, p. 362. John Wiley & Sons Ltd. (1994)
Puget, J.: A fast algorithm for the bound consistency of alldiff constraints. In: Proceedings of the National Conference on Artificial Intelligence, pp. 359–366. John Wiley & Sons Ltd. (1998)
Mackworth, A.: Consistency in networks of relations. Artificial Intelligence 8(1), 99–118 (1977)
Sabin, D., Freuder, E.: Contradicting conventional wisdom in constraint satisfaction. In: Proceedings of the 11th European Conference on Artificial Intelligence, pp. 125–129. Springer, Heidelberg (1994)
Bessière, C., Régin, J.: MAC and Combined Heuristics: Two Reasons to Forsake FC (and CBJ?) on Hard Problems. In: Freuder, E.C. (ed.) CP 1996. LNCS, vol. 1118, pp. 61–75. Springer, Heidelberg (1996)
Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: SATzilla: portfolio-based algorithm selection for SAT. Journal of Artificial Intelligence Research 32(1), 565–606 (2008)
Hutter, F., Hamadi, Y., Hoos, H., Leyton-Brown, K.: Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms. In: Benhamou, F. (ed.) CP 2006. LNCS, vol. 4204, pp. 213–228. Springer, Heidelberg (2006)
Minton, S.: Automatically configuring constraint satisfaction programs: A case study. Constraints 1(1), 7–43 (1996)
Epstein, S., Wallace, R., Freuder, E., Li, X.: Learning propagation policies. In: Proceedings of the Second International Workshop on Constraint Propagation And Implementation, pp. 1–15 (2005)
Gebruers, C., Hnich, B., Bridge, D., Freuder, E.: Using CBR to Select Solution Strategies in Constraint Programming. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 222–236. Springer, Heidelberg (2005)
El Sakkout, H., Wallace, M., Richards, E.: An Instance of Adaptive Constraint Propagation. In: Freuder, E.C. (ed.) CP 1996. LNCS, vol. 1118, pp. 164–178. Springer, Heidelberg (1996)
Stergiou, K.: Heuristics for dynamically adapting propagation. In: Proceedings of the 18th European Conference on Artificial Intelligence, pp. 485–489. IOS Press (2008)
Nadel, B.: Constraint satisfaction algorithms. Computational Intelligence 5(4), 188–224 (1989)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Watts, D., Strogatz, S.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)
Gomes, C., Shmoys, D.: Completing quasigroups or latin squares: A structured graph coloring problem. In: Proceedings of the Computational Symposium on Graph Coloring and Generalizations, pp. 22–39 (2002)
Thompson, C.D.S.: Metareasoning about propagators for constraint satisfaction. Master’s thesis, Department of Computer Science, University of Saskatchewan (2011)
Hall, M.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the 17th International Conference on Machine Learning, pp. 359–366 (2000)
Gent, I., MacIntyre, E., Prosser, P., Walsh, T.: The constrainedness of search. In: Proceedings of the 13th National Conference on Artificial Intelligence, pp. 246–252 (1996)
Cohen, J.: A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1), 37–46 (1960)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Thompson, C.D.S., Horsch, M.C. (2012). Predicting Good Propagation Methods for Constraint Satisfaction. In: Kosseim, L., Inkpen, D. (eds) Advances in Artificial Intelligence. Canadian AI 2012. Lecture Notes in Computer Science(), vol 7310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30353-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-30353-1_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30352-4
Online ISBN: 978-3-642-30353-1
eBook Packages: Computer ScienceComputer Science (R0)