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Integrating Probabilistic Reasoning with Constraint Satisfaction

We hypothesize and confirm that probabilistic reasoning is closely related to constraint satisfaction at a formal level, and that this relationship yields effective algorithms for guiding constraint satisfaction and constraint optimization solvers. 
By taking a unified view of probabilistic inference and constraint reasoning in terms of graphical models, we first associate a number of formalisms and techniques between the two areas. For instance, we characterize search and inference in constraint reasoning as summation and multiplication (or disjunction and conjunction) in the probabilistic space; necessary but insufficient consistency conditions for solutions to constraint problems (like arc-consistency) mirror approximate objective functions over probability distributions (like the Bethe free energy); and the polytope of feasible points for marginal probabilities represents the linear relaxation of a particular constraint satisfaction problem. 
While such insights synthesize an assortment of existing formalisms from varied research communities, they also yield an entirely novel set of “bias estimation” techniques that contribute to a growing body of research on applying probabilistic methods to constraint problems. In practical terms, these techniques estimate the percentage of solutions to a constraint satisfaction or optimization problem wherein a given variable is assigned a given value. By devising search methods that incorporate such information as heuristic guidance for variable and value ordering, we are able to outperform existing solvers on problems of interest from constraint satisfaction and constraint optimization-as represented here by the SAT and MaxSAT problems. 
Further, for MaxSAT we present an equivalent transformation” process that normalizes the weights in constraint optimization problems, in order to encourage prunings of the search tree during branch-and-bound search. To control such computationally expensive processes, we determine promising situations for using them throughout the course of an individual search process. We accomplish this using a reinforcement learning-based control module that seeks a principled balance between the exploration of new strategies and the exploitation of existing experiences.