Detecting Types of Variables for Generalization in Constraint Acquisition

Abstract : During the last decade several constraint acqui- sition systems have been proposed for assisting non-expert users in building constraint programming models. GENACQ is an algorithm based on generalization queries that can be plugged into many constraint acquisition systems. However, generalization queries require the aggregation of variables into types which is not always a simple task for non-expert users. In this paper, we propose a new algorithm that is able to learn types during the constraint acquisition process. The idea is to infer potential types by analyzing the structure of the current constraint network and to use the extracted types to ask generalization queries. Our approach gives good results although no knowledge on the types is provided.
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Communication dans un congrès
ICTAI: International Conference on Tools with Artificial Intelligence, Nov 2015, Vietri sul Mare, Italy. ICTAI 2015 Proceedings, 2015, ICTAI 2015 Proceedings
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01276187
Contributeur : Joël Quinqueton <>
Soumis le : jeudi 18 février 2016 - 22:18:48
Dernière modification le : jeudi 11 janvier 2018 - 06:26:23

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  • HAL Id : lirmm-01276187, version 1

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Abderrazak Daoudi, Nadjib Lazaar, Younes Mechqrane, Christian Bessière, El Houssine Bouyakhf. Detecting Types of Variables for Generalization in Constraint Acquisition. ICTAI: International Conference on Tools with Artificial Intelligence, Nov 2015, Vietri sul Mare, Italy. ICTAI 2015 Proceedings, 2015, ICTAI 2015 Proceedings. 〈lirmm-01276187〉

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