Scenario-based Stochastic Constraint Programming
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 257-261.
Abstract
To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in [Walsh, 2002] along a number of important dimensions (e.g. to multiple chance constraints and to a range of new objectives). We also provide a new (but equivalent) semantics based on scenarios. Using this semantics, we can compile stochastic constraint programs down into conventional (nonstochastic) constraint programs. This allows us to exploit the full power of existing constraint solvers. We have implemented this framework for decision making under uncertainty in stochastic OPL, a language which is based on the OPL constraint modelling language [Hentenryck et al. , 1999]. To illustrate the potential of this framework, we model a wide range of problems in areas as diverse as finance, agriculture and production.
@inproceedings{ijcai2003p38,
title = {Scenario-based Stochastic Constraint Programming},
author = {Manandhar, Suresh and Tarim, Armagan and Walsh, Toby},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {257--261},
year = {2003},
url = {https://ijcai.org/proceedings/2003/38},
}
TY - CPAPER TI - Scenario-based Stochastic Constraint Programming AU - Manandhar, Suresh AU - Tarim, Armagan AU - Walsh, Toby T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 257 EP - 261 UR - https://ijcai.org/proceedings/2003/38 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
