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Conference Papers Year : 2013

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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Dates and versions

lirmm-00830325 , version 1 (04-06-2013)

Identifiers

  • HAL Id : lirmm-00830325 , version 1
  • PRODINRA : 263217

Cite

Christian Bessiere, 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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