Problem Transformations and Algorithm Selection for CSPs

Our initial line of research has shown that, to achieve the best performance on a constraint satisfaction problem, it may be beneficial to translate it to a satisfiability problem. For this translation, it is important to choose both the encoding and satisfiability solver in combination. By doing so, the contrasting performance among solvers on different representations of the same problem can be exploited. In taking these considerations into account, the performance of a solver portfolio augmented with multiple problem transformations can be improved significantly compared to restricting the portfolio to a single problem representation.