The modelling and reformulation of constraint networks are recognised as important problems. The task of automatically acquiring a constraint network formulation of a problem from a subset of its solutions and non-solutions has been presented in the literature. However, the choice of such a subset was assumed to be made independently of the acquisition process. We present an approach in which an interactive acquisition system actively selects a good set of examples. We show that the number of examples required to acquire a constraint network is significantly reduced using our approach.

Authors

• Christian Bessiere
• Remi Coletta
• Barry O'Sullivan
• Mathias Paulin

Keywords

No keywords are indexed for this paper.