Stochastic Constraint Programming

TL;DR

Stochastic constraint programming is introduced as a new framework for modeling combinatorial decision problems with uncertainty, combining features of constraint satisfaction and stochastic optimization, with algorithms and extensions discussed.

Contribution

The paper presents the formal semantics, algorithms, and extensions for stochastic constraint programming, integrating decision and stochastic variables.

Findings

Proposed a formal semantics for stochastic constraint programs.

Developed complete algorithms and approximation procedures.

Compared stochastic constraint programming with other decision-making approaches.

Abstract

To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision variables (which we can set) and stochastic variables (which follow a probability distribution). They combine together the best features of traditional constraint satisfaction, stochastic integer programming, and stochastic satisfiability. We give a semantics for stochastic constraint programs, and propose a number of complete algorithms and approximation procedures. Finally, we discuss a number of extensions of stochastic constraint programming to relax various assumptions like the independence between stochastic variables, and compare with other approaches for decision making under uncertainty.