Constraint Acquisition via Partial Queries - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2013

Constraint Acquisition via Partial Queries


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.
Fichier principal
Vignette du fichier
ijcai13-quacq.pdf (286.3 Ko) Télécharger le fichier
Origin Explicit agreement for this submission

Dates and versions

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


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


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⟩
700 View
444 Download


Gmail Mastodon Facebook X LinkedIn More