Multiple Constraint Acquisition
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 698-703.
Abstract
QUACQ is a constraint acquisition system that assists a non-expert user to model her problem as a constraint network by classifying (partial) examples as positive or negative. For each negative example, QUACQ focuses onto a constraint of the target 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 number of constraints violated by a given negative example. Finally we give an experimental evaluation that shows that our approach improves on QUACQ.
@inproceedings{ijcai2016p105,
title = {Multiple Constraint Acquisition},
author = {Arcangioli, Robin and Bessiere, Christian and Lazaar, Nadjib},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {698--703},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/105},
}
TY - CPAPER TI - Multiple Constraint Acquisition AU - Arcangioli, Robin AU - Bessiere, Christian AU - Lazaar, Nadjib T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 698 EP - 703 UR - https://ijcai.org/proceedings/2016/105 ER -Copied
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