Multiple Constraint Acquisition
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. PDF
Cite
Text
Arcangioli et al. "Multiple Constraint Acquisition." International Joint Conference on Artificial Intelligence, 2016.Markdown
[Arcangioli et al. "Multiple Constraint Acquisition." International Joint Conference on Artificial Intelligence, 2016.](https://mlanthology.org/ijcai/2016/arcangioli2016ijcai-multiple/)BibTeX
@inproceedings{arcangioli2016ijcai-multiple,
title = {{Multiple Constraint Acquisition}},
author = {Arcangioli, Robin and Bessiere, Christian and Lazaar, Nadjib},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2016},
pages = {698-704},
url = {https://mlanthology.org/ijcai/2016/arcangioli2016ijcai-multiple/}
}