Compiling Stochastic Constraint Programs to And-Or Decision Diagrams

TL;DR

This paper introduces a method to compile factored stochastic constraint programs into compact decision diagrams, improving efficiency by exploiting identical subproblems and outperforming existing approaches.

Contribution

It presents a novel compilation technique that transforms And-Or search trees into decision diagrams for more efficient solving of FSCPs.

Findings

Significant reduction in problem size through subproblem sharing

Improved search efficiency over existing methods

Effective representation of solutions using decision diagrams

Abstract

Factored stochastic constraint programming (FSCP) is a formalism to represent multi-stage decision making problems under uncertainty. FSCP models support factorized probabilistic models and involve constraints over decision and random variables. These models have many applications in real-world problems. However, solving these problems requires evaluating the best course of action for each possible outcome of the random variables and hence is computationally challenging. FSCP problems often involve repeated subproblems which ideally should be solved once. In this paper we show how identifying and exploiting these identical subproblems can simplify solving them and leads to a compact representation of the solution. We compile an And-Or search tree to a compact decision diagram. Preliminary experiments show that our proposed method significantly improves the search efficiency by reducing the size of the problem and outperforms the existing methods.