Finite Domain Constraint Solver Learning
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 1379-1379.
Abstract
In this paper, we present an abstract framework for learning a finite domain constraint solver modeled by a set of operators enforcing a consistency. The behavior of the consistency to be learned is taken as the set of examples on which the learning process is applied. The best possible expression of this operator in a given language is then searched. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.
Arnaud Lallouet, Thi-Bich-Hanh Dao, Andrei Legtchenko, and AbdelAli Ed-Dbali. 2003. Finite Domain Constraint Solver Learning. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03. IJCAI, 1379–1379. https://ijcai.org/proceedings/2003/205
Lallouet, A., Dao, T., Legtchenko, A., & Ed-Dbali, A. (2003). Finite Domain Constraint Solver Learning. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 (pp. 1379–1379). IJCAI. https://ijcai.org/proceedings/2003/205
@inproceedings{ijcai2003p205,
title = {Finite Domain Constraint Solver Learning},
author = {Lallouet, Arnaud and Dao, Thi-Bich-Hanh and Legtchenko, Andrei and Ed-Dbali, AbdelAli},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {1379--1379},
year = {2003},
url = {https://ijcai.org/proceedings/2003/205},
}
TY - CPAPER TI - Finite Domain Constraint Solver Learning AU - Lallouet, Arnaud AU - Dao, Thi-Bich-Hanh AU - Legtchenko, Andrei AU - Ed-Dbali, AbdelAli T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 1379 EP - 1379 UR - https://ijcai.org/proceedings/2003/205 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: