Multiple Constraint Aquisition

Robin Arcangioli 1 Christian Bessière 1 Nadjib Lazaar 1
1 COCONUT - Agents, Apprentissage, Contraintes
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : QUACQ is a constraint acquisition system that as- sists a non-expert user to model her problem as a constraint network by classifying (partial) exam- ples as positive or negative. For each negative ex- ample, QUACQ focuses onto a constraint of the tar- get network. The drawback is that the user may need to answer a great number of such examples to learn all the constraints. In this paper, we provide a new approach that is able to learn a maximum num- ber of constraints violated by a given negative ex- ample. Finally we give an experimental evaluation that shows that our approach improves on QUACQ.
Keywords : Artificail Intelligence
Type de document :
Communication dans un congrès
IJCAI: International Joint Conference on Artificial Intelligence, Jul 2016, New York City, United States. 25th International Joint Conference on Artificial Intelligence, pp.698-704, 2016, 〈http://ijcai-16.org/〉
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Soumis le : vendredi 30 septembre 2016 - 21:20:35
Dernière modification le : dimanche 16 décembre 2018 - 10:42:02
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Robin Arcangioli, Christian Bessière, Nadjib Lazaar. Multiple Constraint Aquisition. IJCAI: International Joint Conference on Artificial Intelligence, Jul 2016, New York City, United States. 25th International Joint Conference on Artificial Intelligence, pp.698-704, 2016, 〈http://ijcai-16.org/〉. 〈lirmm-01374712〉

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