Abstract
In constraint programming there are often many choices regarding the propagation method to be used on the constraints of a problem. However, simple constraint solvers usually only apply a standard method, typically (generalized) arc consistency, on all constraints throughout search. Advanced solvers additionally allow for the modeler to choose among an array of propagators for certain (global) constraints. Since complex interactions exist among constraints, deciding in the modelling phase which propagation method to use on given constraints can be a hard task that ideally we would like to free the user from. In this paper we propose a simple technique towards the automation of this task. Our approach exploits information gathered from a random probing preprocessing phase to automatically decide on the propagation method to be used on each constraint. As we demonstrate, data gathered though probing allows for the solver to accurately differentiate between constraints that offer little pruning as opposed to ones that achieve many domain reductions, and also to detect constraints and variables that are amenable to certain propagation methods. Experimental results from an initial evaluation of the proposed method on binary CSPs demonstrate the benefits of our approach.
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
Balafoutis, T., Stergiou, K.: Exploiting constraint weights for revision ordering in Arc Consistency Algorithms. In: ECAI 2008 Workshop on Modeling and Solving Problems with Constraints (2008)
Beck, C.: Solution-Guided Multi-Point Constructive Search for Job Shop Scheduling. JAIR 29, 49–77 (2007)
Beck, C., Prosser, P., Wallace, R.: Variable Ordering Heuristics Show Promise. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 711–715. Springer, Heidelberg (2004)
Bessiere, C.: Constraint propagation. In: Rossi, F., van Beek, P., Walsh, T. (eds.) Handbook of Constraint Programming, ch. 3. Elsevier, Amsterdam (2006)
Bezdek, J.C. (ed.): Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)
Boussemart, F., Hemery, F., Lecoutre, C.: Revision ordering heuristics for the Constraint Satisfaction Problem. In: CP 2004 Workshop on Constraint Propagation and Implementation (2004)
Boussemart, F., Heremy, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: ECAI 2004, pp. 482–486 (2004)
Chmeiss, A., Sais, L.: Constraint Satisfaction Problems: Backtrack Search Revisited. In: ICTAI 2004, pp. 252–257 (2004)
Debruyne, R., Bessière, C.: From restricted path consistency to max-restricted path consistency. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 312–326. Springer, Heidelberg (1997)
El Sakkout, H., Wallace, M., Richards, B.: An Instance of Adaptive Constraint Propagation. In: Freuder, E.C. (ed.) CP 1996. LNCS, vol. 1118, pp. 164–178. Springer, Heidelberg (1996)
Freuder, E., Wallace, R.J.: Selective relaxation for constraint satisfaction problems. In: ICTAI 1996 (1996)
Grimes, D., Wallace, R.J.: Sampling Strategies and Variable Selection in Weighted Degree Heuristics. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 831–838. Springer, Heidelberg (2007)
Laburthe, F., Ocre: Choco: implementation du noyau d’un systeme de contraintes. In: JNPC 2000, pp. 151–165 (2000)
Lecoutre, C., Prosser, P.: Maintaining Singleton Arc Consistency. In: 3rd International Workshop on Constraint Propagation And Implementation (CPAI 2006), pp. 47–61 (2006)
Mehta, D., van Dongen, M.R.C.: Probabilistic Consistency Boosts MAC and SAC. In: IJCAI 2007, pp. 143–148 (2007)
Ruml, W.: Incomplete Tree Search using Adaptive Probing. In: IJCAI 2001, pp. 235–241 (2001)
Schulte, C., Stuckey, P.J.: Speeding Up Constraint Propagation. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 619–633. Springer, Heidelberg (2004)
Schulte, C., Stuckey, P.J.: Efficient Constraint Propagation Engines. ACM Trans. Program. Lang. Syst. 31(1), 1–43 (2008)
Stergiou, K.: Heuristics for Dynamically Adapting Propagation. In: ECAI 2008, pp. 485–489 (2008)
Szymanek, R., Lecoutre, C.: Constraint-Level Advice for Shaving. In: ICLP 2008, pp. 636–650 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stamatatos, E., Stergiou, K. (2009). Learning How to Propagate Using Random Probing. In: van Hoeve, WJ., Hooker, J.N. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2009. Lecture Notes in Computer Science, vol 5547. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01929-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-01929-6_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01928-9
Online ISBN: 978-3-642-01929-6
eBook Packages: Computer ScienceComputer Science (R0)