  The Constraint Satisfaction Problem (CSP) framework offers a simple and sound
basis for representing and solving simple decision problems, without
uncertainty. This paper is devoted to an extension of the CSP framework
enabling us to deal with some decisions problems under uncertainty. This
extension relies on a differentiation between the agent-controllable decision
variables and the uncontrollable parameters whose values depend on the
occurrence of uncertain events. The uncertainty on the values of the parameters
is assumed to be given under the form of a probability distribution. Two
algorithms are given, for computing respectively decisions solving the problem
with a maximal probability, and conditional decisions mapping the largest
possible amount of possible cases to actual decisions.
