  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.
