ML-based Decision Support for CSP Modelling with Regular Membership and Table Constraints

Sven Löffler, Ilja Becker, Petra Hofstedt

2021

Abstract

The regular membership and the table constraints are very powerful constraints which allow it to substitute every other constraint in a constraint satisfaction problem. Both constraints can be used very flexible in a huge amount of problems. The main question we want to answer with this paper is, when is it faster to use the regular membership constraint, and when the table constraint. We use a machine learning approach for such a prediction based on propagation times. As learning input it takes randomly generated constraint problems, each containing exactly one table resp. one regular membership constraint. The evaluation of the resulting decision tool with specific but randomly generated CSPs shows the usefulness of our approach.

Paper Citation

in Harvard Style

Löffler S., Becker I. and Hofstedt P. (2021). ML-based Decision Support for CSP Modelling with Regular Membership and Table Constraints.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 974-981. DOI: 10.5220/0010299109740981

in Bibtex Style

@conference{icaart21,
author={Sven Löffler and Ilja Becker and Petra Hofstedt},
title={ML-based Decision Support for CSP Modelling with Regular Membership and Table Constraints},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={974-981},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010299109740981},
isbn={978-989-758-484-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - ML-based Decision Support for CSP Modelling with Regular Membership and Table Constraints
SN - 978-989-758-484-8
AU - Löffler S.
AU - Becker I.
AU - Hofstedt P.
PY - 2021
SP - 974
EP - 981
DO - 10.5220/0010299109740981