  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.
