Automatically Decomposing Configuration Problems

Configuration was one of the first tasks successfully approached via AI techniques. However, solving configuration problems can be computationally expensive. In this work, we show that the decomposition of a configuration problem into a set of simpler and independent subproblems can decrease the computational cost of solving it. In particular, we describe a novel decomposition technique exploiting the compositional structure of complex objects and we show experimentally that such a decomposition can improve the efficiency of configurators.