Stochastic Constraint Propagation for Mining Probabilistic Networks

Stochastic Constraint Propagation for Mining Probabilistic Networks

Anna Louise D. Latour, Behrouz Babaki, Siegfried Nijssen

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 1137-1145. https://doi.org/10.24963/ijcai.2019/159

A number of data mining problems on probabilistic networks can be modeled as Stochastic Constraint Optimization 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 (sub-)linear in the size of the OBDD, and maintains domain consistency. We experimentally evaluate the effectiveness of this global constraint in comparison to existing decomposition-based approaches, and show how this propagator can be used in combination with another data mining specific constraint present in CP systems. As test cases we use problems from the data mining literature.
Keywords:
Constraints and SAT: Global Constraints
Uncertainty in AI: Exact Probabilistic Inference
Heuristic Search and Game Playing: Combinatorial Search and Optimisation
Uncertainty in AI: Uncertainty in AI