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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.
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Submitted on : Friday, September 30, 2016 - 9:20:35 PM
Last modification on : Monday, October 11, 2021 - 1:24:08 PM
Long-term archiving on: : Saturday, December 31, 2016 - 4:34:00 PM


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  • HAL Id : lirmm-01374712, version 1



Robin Arcangioli, Christian Bessière, Nadjib Lazaar. Multiple Constraint Aquisition. IJCAI: International Joint Conference on Artificial Intelligence, Jul 2016, New York City, United States. pp.698-704. ⟨lirmm-01374712⟩



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