A Constraint Satisfaction Approach to Decision under Uncertainty

TL;DR

This paper extends the CSP framework to handle decision problems under uncertainty by differentiating controllable and uncontrollable variables, incorporating probabilistic information, and proposing algorithms for maximizing decision success probability.

Contribution

It introduces a novel extension of CSP for uncertain decision problems, including algorithms for probabilistic decision optimization.

Findings

Algorithms for maximizing decision probability

Framework for decision under uncertainty with probabilistic parameters

Extension of CSP to uncertain decision problems

Abstract

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.