Machine Learning
Machine Learning
Suggestion Strategies for Constraint-Based Matchmaker Agents
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Automatic Design of Robot Behaviors through Constraint Network Acquisition
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
QUICKXPLAIN: preferred explanations and relaxations for over-constrained problems
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Query-driven constraint acquisition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On Learning Constraint Problems
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
A SAT-based version space algorithm for acquiring constraint satisfaction problems
ECML'05 Proceedings of the 16th European conference on Machine Learning
A model seeker: extracting global constraint models from positive examples
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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We learn constraint networks by asking the user partial queries. That is, we ask the user to classify assignments to subsets of the variables as positive or negative. We provide an algorithm that, given a negative example, focuses onto a constraint of the target network in a number of queries logarithmic in the size of the example. We give information theoretic lower bounds for learning some simple classes of constraint networks and show that our generic algorithm is optimal in some cases. Finally we evaluate our algorithm on some benchmarks.