Abstract
Stochastic Constraint Programming is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. A solution to such a problem is a policy tree that specifies decision variable assignments in each scenario. Several solution methods have been proposed but none seems practical for large multi-stage problems. We propose an incomplete approach: specifying a policy tree indirectly by a parameterised function, whose parameter values are found by evolutionary search. On some problems this method is orders of magnitude faster than a state-of-the-art scenario-based approach, and it also provides a very compact representation of policy trees.
B. Hnich is supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. SOBAG-108K027. This material is based in part upon works supported by the Science Foundation Ireland under Grant No. 05/IN/I886.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Balafoutis, T., Stergiou, K.: Algorithms for Stochastic CSPs. In: Benhamou, F. (ed.) CP 2006. LNCS, vol. 4204, pp. 44–58. Springer, Heidelberg (2006)
Benoist, T., Bourreau, E., Caseau, Y., Rottembourg, B.: Towards Stochastic Constraint Programming: A Study of On-Line Multi-Choice Knapsack with Deadlines. In: Walsh, T. (ed.) CP 2001. LNCS, vol. 2239, pp. 61–76. Springer, Heidelberg (2001)
Beyer, H.-G.: Evolutionary Algorithms in Noisy Environments: Theoretical Issues and Guidelines for Practice. Computer Methods in Applied Mechanics and Engineering 186(2-4), 239–267 (2000)
Bordeaux, L., Samulowitz, H.: On the Stochastic Constraint Satisfaction Framework. In: ACM Symposium on Applied Computing, pp. 316–320 (2007)
Fogel, D.B., Chellapilla, K.: Verifying Anaconda’s Expert Rating by Competing Against Chinook: Experiments in Co-Evolving a Neural Checkers Player. Neurocomputing 42(1-4), 69–86 (2002)
Fogel, D.B., Hays, T.J., Hahn, S.L., Quon, J.: A Self-Learning Evolutionary Chess Program. Proceedings of the IEEE 92(12), 1947–1954 (2004)
Gomez, F., Schmidhuber, J., Miikkulainen, R.: Efficient Non-Linear Control Through Neuroevolution. Journal of Machine Learning Research 9, 937–965 (2008)
Hewahi, N.M.: Engineering Industry Controllers Using Neuroevolution. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19(1), 49–57 (2005)
Jin, Y., Branke, J.: Evolutionary Optimization in Uncertain Environments — a Survey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)
Littman, M.L., Majercik, S.M., Pitassi, T.: Stochastic Boolean Satisfiability. Journal of Automated Reasoning 27(3), 251–296 (2001)
Lubberts, A., Miikkulainen, R.: Co-Evolving a Go-Playing Neural Network. In: Genetic and Evolutionary Computation Conference, pp. 14–19. Kaufmann, San Francisco (2001)
Majercik, S.M.: APPSSAT: Approximate Probabilistic Planning Using Stochastic Satisfiability. International Journal of Approximate Reasoning 45(2), 402–419 (2007)
Majercik, S.M.: Stochastic Boolean Satisfiability. In: Handbook of Satisfiability, ch. 27, pp. 887–925. IOS Press, Amsterdam (2009)
Pollack, J.B., Blair, A.D.: Co-Evolution in the Successful Learning of Backgammon Strategy. Machine Learning 32(3), 225–240 (1998)
Prestwich, S.D., Tarim, S.A., Rossi, R., Hnich, B.: A Steady-State Genetic Algorithm With Resampling for Noisy Inventory Control. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 559–568. Springer, Heidelberg (2008)
Rossi, R., Tarim, S.A., Hnich, B., Prestwich, S.D.: Cost-Based Domain Filtering for Stochastic Constraint Programming. In: Stuckey, P.J. (ed.) CP 2008. LNCS, vol. 5202, pp. 235–250. Springer, Heidelberg (2008)
Stanley, K.O., Miikkulainen, R.: Evolving Neural Networks Through Augmenting Topologies. Evolutionary Computation 10(2), 99–127 (2002)
Tarim, S.A., Manandhar, S., Walsh, T.: Stochastic Constraint Programming: A Scenario-Based Approach. Constraints 11(1), 1383–7133 (2006)
Tarim, S.A., Miguel, I.: A Hybrid Bender’s Decomposition Method for Solving Stochastic Constraint Programs with Linear Recourse. In: Hnich, B., Carlsson, M., Fages, F., Rossi, F. (eds.) CSCLP 2005. LNCS (LNAI), vol. 3978, pp. 133–148. Springer, Heidelberg (2006)
Walsh, T.: Stochastic Constraint Programming. In: 15th European Conference on Artificial Intelligence (2002)
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
Prestwich, S., Tarim, S.A., Rossi, R., Hnich, B. (2009). Evolving Parameterised Policies for Stochastic Constraint Programming. In: Gent, I.P. (eds) Principles and Practice of Constraint Programming - CP 2009. CP 2009. Lecture Notes in Computer Science, vol 5732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04244-7_53
Download citation
DOI: https://doi.org/10.1007/978-3-642-04244-7_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04243-0
Online ISBN: 978-3-642-04244-7
eBook Packages: Computer ScienceComputer Science (R0)