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.
R. Arcangioli, N. Lazaar. Multiple Constraint Acquisition.IJCAI workshop on Intelligent Personalization IP'15. [paper]
C. Bessiere, R. Coletta,A. Daoudi, E. Hebrard, G. Katsirelos, N. Lazaar, Y. Mechqrane, N. Narodytska, C.G. Quimper, and T. Walsh. New Approaches to Constraint Acquisition.ICON book chapter.
C. Bessiere, R. Coletta, N. Lazaar. Solve a Constraint Problem Without Modeling It. ICTAI'2014. [paper][slides]
C. Bessiere, R. Coletta, A. Daoudi, N. Lazaar, Y. Mechqrane, E. Bouyakhf. Boosting Constraint Acquisition via Generalization Queries. ECAI'2014. [paper][slides]
A. Daoudi, C. Bessiere, R. Coletta, N. Lazaar, Y. Mechqrane, E. Bouyakhf. Acquisition de contraintes par requêtes de généralisation. JFPC'2014. [paper][slides]
C. Bessiere, R. Coletta, E. Hebrard, G. Katsirelos, N. Lazaar, N. Narodytska, C.G. Quimper, and T. Walsh. Acquisition de contraintes avec des requêtes partielles. JFPC'2014. [paper][slides]
C. Bessiere, R. Coletta, E. Hebrard, G. Katsirelos, N. Lazaar, N. Narodytska, C.G. Quimper, and T. Walsh. Constraint acquisition via partial queries. IJCAI'2013. [paper]
