Summary: Decomposition Strategies for Automatically Solving Configuration
Dipartimento di Informatica, UniversitÓ di Torino
Corso Svizzera 185; 10149 Torino; Italy
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.
Recent development in Web technologies has opened important opportunities for developing new interactive services.
Among others, the possibility for a customer to access a wide variety of information about products or services has
paved the way for systems which embed an intelligent component for supporting the customer in such activity. In most
of these systems there is the implicit assumption that the product or the service cannot be modified to match the user