Scenario-based stochastic constraint programming


Manandhar S., TARIM Ş. A., Walsh T.

18th International Joint Conference on Artificial Intelligence, IJCAI 2003, Acapulco, Mexico, 9 - 15 August 2003, pp.257-262 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Acapulco
  • Country: Mexico
  • Page Numbers: pp.257-262
  • Hacettepe University Affiliated: Yes

Abstract

To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in iWalsh, 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 (non-stochastic) 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.