Theoretical Computer Science - Special issue: Algorithmic learning theory
A SAT-based version space algorithm for acquiring constraint satisfaction problems
ECML'05 Proceedings of the 16th European conference on Machine Learning
Predicting and Learning Executability of Composite Web Services
ICSOC '08 Proceedings of the 6th International Conference on Service-Oriented Computing
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A constraint seeker: finding and ranking global constraints from examples
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Constraint acquisition via partial queries
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.