In this work we introduce a novel approach, based on sampling, for finding policies that are likely to be solutions to complex stochastic constraint satisfaction problems and constraint optimization problems. Our approach reduces the size of the original problem being analyzed and it guarantees that, with a given confidence probability, the policies produced by solving this reduced problem satisfy the chance-constraints in the original model up to the prescribed satisfaction and error tolerance thresholds. To do so, we blend concepts from stochastic programming, constraint programming, applied mathematics, probability theory and statistics. The strategy introduced can be immediately employed in concert with one of the existing approaches for solving stochastic constraint programs. We illustrated our novel approach on a number of stochastic combinatorial optimization problems.
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