Stochastic Constraint Propagation for Mining Probabilistic Networks (extended abstract)

A number of data mining problems on probabilistic networks can be modelled as Stochastic Constraint Optimisation and Satisfaction Problems, i.e., problems that involve objectives or constraints with a stochastic component. Earlier methods for solving these problems used Ordered Binary Decision Diagrams (OBDDs) to represent constraints on probability distributions, which were decomposed into sets of smaller constraints and solved by Constraint Programming (CP) or Mixed Integer Programming (MIP) solvers. For the specific case of monotonic distributions, we propose an alternative method: a new propagator for a global OBDD-based constraint. We show that this propagator is efficient and maintains domain consistency. We experimentally evaluate this global constraint in comparison to existing decomposition-based approaches. As test cases we use problems from the data mining literature.

Published in Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2019

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