Learning When to Use Automatic Tabulation in Constraint Model Reformulation

Combinatorial optimisation has numerous practi-cal applications, such as planning, logistics, or cir-cuit design. Problems such as these can be solved by approaches such as Boolean Satisfiability (SAT) or Constraint Programming (CP). Solver performance is affected significantly by the model chosen to represent a given problem, which has led to the study of model reformulation. One such method is tabulation: rewriting the expression of some of the model constraints in terms of a single table constraint. Successfully applying this process means identifying expressions amenable to trans-formation, which has typically been done manually. Recent work introduced an automatic tabulation using a set of hand-designed heuristics to identify constraints to tabulate. However, the performance of these heuristics varies across problem classes and solvers. Recent work has shown learning techniques to be increasingly useful in the context of automatic model reformulation. The goal of this study is to understand whether it is possible to improve the performance of such heuristics, by learning a model to predict whether or not to activate them for a given instance. Experimental results suggest that a random forest classifier is the most robust choice, improving the performance of four different SAT and CP solvers.