Scenario-based stochastic constraint programming

S. Manandhar; A. Tarim; T. Walsh

Scenario-based stochastic constraint programming

S. Manandhar, A. Tarim, T. Walsh

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the 18th International Joint Conference in Artificial Intelligence
Pages	257-262
Number of pages	5
Publication status	Published - 2003
Event	IJCAI 03 - Acapulco, Mexico Duration: 9 Aug 2003 → 15 Aug 2003

Conference

Conference	IJCAI 03
Country/Territory	Mexico
City	Acapulco
Period	9/08/03 → 15/08/03

Access to Document

http://dli.iiit.ac.in/ijcai/IJCAI-2003/PDF/038.pdf

Cite this

@inproceedings{12d9572a53034b6e9f493badabffa313,

title = "Scenario-based stochastic constraint programming",

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.",

author = "S. Manandhar and A. Tarim and T. Walsh",

year = "2003",

language = "English",

pages = "257--262",

booktitle = "Proceedings of the 18th International Joint Conference in Artificial Intelligence",

note = "IJCAI 03 ; Conference date: 09-08-2003 Through 15-08-2003",

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TY - GEN

T1 - Scenario-based stochastic constraint programming

AU - Manandhar, S.

AU - Tarim, A.

AU - Walsh, T.

PY - 2003

Y1 - 2003

N2 - 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.

AB - 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.

M3 - Conference contribution

SP - 257

EP - 262

BT - Proceedings of the 18th International Joint Conference in Artificial Intelligence

T2 - IJCAI 03

Y2 - 9 August 2003 through 15 August 2003

ER -