A Constraint Satisfaction Approach to Decision under Uncertainty

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 frame­ work enabling us to deal with some decisions problems under uncertainty. This extension relies on a differen­ tiation between the agent-controllable decision vari­ ables and the uncontrollable parameters whose values depend on the occurrence of uncertain events. The un­ certainty 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 proba­ bility, and conditional decisions mapping the largest possible amount of possible cases to actual decisions.