Decomposition Strategies for Automatically Solving 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 mutually independent subproblems can decrease the computational cost of solving it. In particular, we describe a decomposition technique exploiting the compositional structure of complex objects (i.e. objects composed by other objects) and we show experimentally that such a decomposition can improve the efficiency of configurators. The master’s thesis [Anselma 02] this work is based on has been awarded with a Honourable Mention for the AI*IA Prize for recent university graduates.