Abstract
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.
This work has been partially supported by ASI (Italian Space Agency).
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Anselma, L., Magro, D., Torasso, P. (2003). Automatically Decomposing Configuration Problems. In: Cappelli, A., Turini, F. (eds) AI*IA 2003: Advances in Artificial Intelligence. AI*IA 2003. Lecture Notes in Computer Science(), vol 2829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39853-0_4
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DOI: https://doi.org/10.1007/978-3-540-39853-0_4
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