  A number of problems in relational Artificial Intelligence can be viewed as
Stochastic Constraint Optimization Problems (SCOPs). These are constraint
optimization problems that involve objectives or constraints with a stochastic
component. Building on the recently proposed language SC-ProbLog for modeling
SCOPs, we propose a new method for solving these problems. Earlier methods used
Probabilistic Logic Programming (PLP) techniques to create Ordered Binary
Decision Diagrams (OBDDs), which were decomposed into smaller constraints in
order to exploit existing constraint programming (CP) solvers. We argue that
this approach has as drawback that a decomposed representation of an OBDD does
not guarantee domain consistency during search, and hence limits the efficiency
of the solver. For the specific case of monotonic distributions, we suggest an
alternative method for using CP in SCOP, based on the development of a new
propagator; we show that this propagator is linear in the size of the OBDD, and
has the potential to be more efficient than the decomposition method, as it
maintains domain consistency.
