Abstract
In Constraint Programming, enumeration strategies play an important role, they can significantly impact the performance of the solving process. However, choosing the right strategy is not simple as its behavior is commonly unpredictable. Autonomous search aims at tackling this concern, it proposes to replace bad performing strategies by more promising ones during the resolution. This process yields a combination of enumeration strategies that worked during the search phase. In this paper, we focus on the study of this combination by carefully tracking the resolution. Our preliminary goal is to find good enumeration strategy blends for a given Constraint Satisfaction Problem.
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Best Blends Experiments, http://www.inf.ucv.cl/~rsoto/best_blends (visited November 2011)
Barták, R., Rudová, H.: Limited assignments: A new cutoff strategy for incomplete depth-first search. In: Proceedings of the 20th ACM Symposium on Applied Computing (SAC), pp. 388–392 (2005)
Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: Proceedings of the 16th Eureopean Conference on Artificial Intelligence (ECAI), pp. 146–150. IOS Press (2004)
Crawford, B., Castro, C., Monfroy, E.: Using a Choice Function for Guiding Enumeration in Constraint Solving. In: Proceedings of the 9th Mexican International Conference on Artificial Intelligence (MICAI), pp. 37–42. IEEE Computer Society (2010)
Crawford, B., Soto, R., Castro, C., Monfroy, E.: A Hyperheuristic Approach for Dynamic Enumeration Strategy Selection in Constraint Satisfaction. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2011, Part II. LNCS, vol. 6687, pp. 295–304. Springer, Heidelberg (2011)
Crawford, B., Soto, R., Castro, C., Monfroy, E.: Extensible CP-Based Autonomous Search. In: Stephanidis, C. (ed.) Posters, HCII 2011, Part I. CCIS, vol. 173, pp. 561–565. Springer, Heidelberg (2011)
Crawford, B., Soto, R., Castro, C., Monfroy, E., Paredes, F.: An Extensible Autonomous Search Framework for Constraint Programming. Int. J. Phys. Sci. 6(14), 3369–3376 (2010)
Crawford, B., Soto, R., Montecinos, M., Castro, C., Monfroy, E.: A Framework for Autonomous Search in the Eclipse Solver. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds.) IEA/AIE 2011, Part I. LNCS, vol. 6703, pp. 79–84. Springer, Heidelberg (2011)
Epstein, S.L., Freuder, E.C., Wallace, R.J., Morozov, A., Samuels, B.: The Adaptive Constraint Engine. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 525–542. Springer, Heidelberg (2002)
Grimes, D., Wallace, R.J.: Learning to identify global bottlenecks in constraint satisfaction search. In: Proceedings of the Twentieth International Florida Artificial Intelligence Research Society (FLAIRS) Conference, pp. 592–597. AAAI Press (2007)
Hamadi, Y., Monfroy, E., Saubion, F.: Special issue on autonomous search. Contraint Programming Letters 4 (2008)
Rossi, F., van Beek, P., Walsh, T.: Handbook of Constraint Programming. Elsevier (2006)
Soubeiga, E.: Development and Application of Hyperheuristics to Personnel Scheduling. PhD thesis, University of Nottingham School of Computer Science (2009)
Wallace, R.J., Grimes, D.: Experimental studies of variable selection strategies based on constraint weights. J. Algorithms 63(1-3), 114–129 (2008)
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Soto, R., Crawford, B., Monfroy, E., Bustos, V. (2012). Using Autonomous Search for Generating Good Enumeration Strategy Blends in Constraint Programming. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7335. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31137-6_46
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DOI: https://doi.org/10.1007/978-3-642-31137-6_46
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