IJCAI 2023poster6 citations
Learning Constraint Networks over Unknown Constraint Languages
Christian Bessiere, Clément Carbonnel, Areski Himeur
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.
Constraint Satisfaction and Optimization: CSO: Constraint learning and acquisitionConstraint Satisfaction and Optimization: CSO: Constraint programming
BibTeX
@inproceedings{ijcai2023p208,
title = {Learning Constraint Networks over Unknown Constraint Languages},
author = {Bessiere, Christian and Carbonnel, Clément and Himeur, Areski},
booktitle = {Proceedings of the Thirty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-23}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Edith Elkind},
pages = {1876--1883},
year = {2023},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2023/208},
url = {https://doi.org/10.24963/ijcai.2023/208},
}