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Constraint Acquisition via Partial Queries

Abstract : 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.
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Contributor : Joël Quinqueton <>
Submitted on : Tuesday, June 4, 2013 - 4:52:25 PM
Last modification on : Monday, July 27, 2020 - 10:32:02 AM
Long-term archiving on: : Tuesday, April 4, 2017 - 4:53:03 PM


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  • HAL Id : lirmm-00830325, version 1


Christian Bessière, Remi Coletta, Emmanuel Hébrard, George Katsirelos, Nadjib Lazaar, et al.. Constraint Acquisition via Partial Queries. IJCAI: International Joint Conference on Artificial Intelligence, Aug 2013, Beijing, China. pp.475-481. ⟨lirmm-00830325⟩



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