Synthesizing Filtering Algorithms in Stochastic Constraint Programming Brahim Hnich,1 Roberto Rossi,2 S. Armagan Tarim,3 Steven Prestwich4 1 Faculty of Computer Science, Izmir University of Economics, Turkey [email protected] 2 Logistics, Decision and Information Sciences, Wageningen UR, the Netherlands [email protected] 3 Department of Management, Hacettepe University, Ankara, Turkey [email protected] 4 Cork Constraint Computation Centre - CTVR, University College, Cork, Ireland [email protected] Keywords: Stochastic Constraint Programming, Global Chance-Constraints, Uncertainty Representation An interesting topic that recently surged at the borderline of Operations Research (OR) and Constraint Programming (CP) is the connection and the integration of Stochastic Programming (SP) and CP. A trend of research that proposes to apply CP to multi-stage decision problems under uncertainty was firstly outlined in [1]. A novel framework able to capture the notion of Stochastic Constraint Satisfaction Problem (SCSP) was proposed by Walsh in [5]. A scenario-based approach for modeling and solving realistic SCSPs has been subsequently proposed by Tarim et al. in [4]. Stochastic Constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a P-Space task. Tarim’s solution approach compiles down SCSPs into classical CSPs. This allows the reuse of classical constraint solvers to solve SCSPs, but at the cost of increased space requirements and weak constraint propagation. This work tries to overcome some of these drawbacks by automatically synthesizing filtering algorithms for global chance-constraints. Global chance-constraints [3] are the natural extension of global constraints for modeling chance-constraints [2] among a non-predefined number of decision and random variables in stochastic constraint programs. The filtering algorithms we propose are parameterized by propagators for the deterministic version of the chance-constraints. Our approach allows the reuse of existing propagators in current constraint solvers and it enhances constraint propagation. Our computational experience show the benefits of this novel approach.

References [1] T. Benoist, E. Bourreau, Y. Caseau, and B. Rottembourg. Towards stochastic constraint programming: A study of online multi-choice knapsack with deadlines. In Toby Walsh, editor, Principles and Practice of Constraint Programming - CP 2001, 7th International Conference, CP 2001, Paphos, Cyprus, November 26 - December 1, 2001, Proceedings, volume 2239 of Lecture Notes in Computer Science, pages 61–76. Springer, 2001. [2] A. Charnes and W. W. Cooper. Chance-constrainted programming. Management Science, 6(1):73–79, 1959. 1

[3] R. Rossi, S. A. Tarim, B. Hnich, and S. Prestwich. A global chance-constraint for stochastic inventory systems under service level constraints. Constraints, 13(4):490– 517, 2008. [4] S. A. Tarim, S. Manandhar, and T. Walsh. Stochastic constraint programming: A scenario-based approach. Constraints, 11(1):53–80, 2006. [5] T. Walsh. Stochastic constraint programming. In Frank van Harmelen, editor, European Conference on Artificial Intelligence, ECAI’2002, Proceedings, pages 111–115. IOS Press, 2002.

2

Synthesizing Filtering Algorithms in Stochastic ... - Roberto Rossi

... constraint programming. In Frank van Harmelen, editor, Euro- pean Conference on Artificial Intelligence, ECAI'2002, Proceedings, pages 111–115. IOS. Press ...

31KB Sizes 0 Downloads 148 Views

Recommend Documents

Stochastic Constraint Programming by ... - Dr Roberto Rossi
1Cork Constraint Computation Centre, University College Cork, Ireland. 2Department of ... 4Faculty of Computer Science, Izmir University of Economics, Turkey.

A Neuroevolutionary Approach to Stochastic Inventory ... - Roberto Rossi
Sep 10, 2010 - roevolutionary approach: using an artificial neural network to ... in (Fu 2002), and a tutorial and survey of the application of SO to inventory control ...... lution. Artificial Intelligence for Engineering Design, Analysis and Manufa

Constraint Programming for Optimization under ... - Roberto Rossi
Sep 10, 2008 - Roberto Rossi1. 1Cork Constraint Computation Centre, University College Cork, Ireland ... approaches computer science has yet made to the Holy Grail of programming: ...... Generating good LB during the search. 65. 62. 130.

Computing the Non-Stationary Replenishment Cycle ... - Roberto Rossi
Feb 6, 2010 - an optimization model is a relevant and novel contribution. ..... rithms, constraint solvers also feature some sort of heuristic search engine (e.g..

A Steady-State Genetic Algorithm With Resampling for ... - Roberto Rossi
1 Cork Constraint Computation Centre, University College, Cork, Ireland ... 3 Faculty of Computer Science, Izmir University of Economics, Turkey.

RESEARCH ARTICLE Process redesign for effective ... - Roberto Rossi
Oct 26, 2012 - and Business Economics, University of Edinburgh, 29 Buccleuch Place, EH8 9JS, ... In a literature review on quantitative operations management approaches in food sup- ply chains ...... Software process simulation modeling:.

Generalised filtering and stochastic DCM for fMRI
This paper is about the fitting or inversion of dynamic causal models (DCMs) of fMRI time series. It tries to establish the validity of stochastic DCMs that accommodate random fluctuations in hidden neuronal and physiological states. We compare and c

Design and Simulation of Adaptive Filtering Algorithms for Active ...
Keywords: Adaptive Filter, LMS Algorithm, Active Noise cancellation, MSE, .... The anti-noise generated corresponding to the white noise is as shown below,.

Two Phase Stochastic Local Search Algorithms for the Biobjective ...
Aug 20, 2007 - We call this method PLS2. 2.2.2 Memetic algorithm ... tive space to the line which connects the starting and the guiding solution is selected.

ASwatch - Roberto Perdisci
Internet service provider that willingly hosts and protects il- licit activities. .... rest of the internet. Changing providers is necessary because a legitimate upstream provider typically responds (albeit of- ten slowly) to repeated abuse complaint

ASwatch - Roberto Perdisci
reputation in the peering decision process (e.g. charge higher a low reputation customer, or even de-peer early). (3) Law enforcement practitioners may prioritize their investigations and start early monitoring on ASes, which will likely need remedia