Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [12 references]  Display  Hide  Download
Contributor : Joël Quinqueton Connect in order to contact the contributor
Submitted on : Tuesday, June 4, 2013 - 4:52:25 PM
Last modification on : Tuesday, October 19, 2021 - 11:17:53 PM
Long-term archiving on: : Tuesday, April 4, 2017 - 4:53:03 PM


Explicit agreement for this submission


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


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⟩



Record views


Files downloads