Learning Constraint Networks over Unknown Constraint Languages
Abstract
Constraint acquisition is the task of learning a constraint network from examples of solutions and non-solutions. Existing constraint acquisition systems typically require advance knowledge of the target network's constraint language, which significantly narrows their scope of applicability. In this paper we propose a constraint acquisition method that computes a suitable constraint language as part of the learning process, eliminating the need for any advance knowledge. We report preliminary experiments on various acquisition benchmarks.
Cite
Text
Bessiere et al. "Learning Constraint Networks over Unknown Constraint Languages." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/208Markdown
[Bessiere et al. "Learning Constraint Networks over Unknown Constraint Languages." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/bessiere2023ijcai-learning/) doi:10.24963/IJCAI.2023/208BibTeX
@inproceedings{bessiere2023ijcai-learning,
title = {{Learning Constraint Networks over Unknown Constraint Languages}},
author = {Bessiere, Christian and Carbonnel, Clément and Himeur, Areski},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {1876-1883},
doi = {10.24963/IJCAI.2023/208},
url = {https://mlanthology.org/ijcai/2023/bessiere2023ijcai-learning/}
}