Exploiting Short Supports for Generalised Arc Consistency for Arbitrary Constraints
Abstract
Special-purpose constraint propagation algorithms (such as those for the element constraint) frequently make implicit use of short supports — by examining a subset of the variables, they can infer support for all other variables and values and save substantial work. However, to date general purpose propagation algorithms (such as GAC-Schema) rely upon supports involving all variables. We demonstrate how to employ short supports in a new general purpose propagation algorithm called ShortGAC. This works when provided with either an explicit list of allowed short tuples, or a function to calculate the next supporting short tuple. Empirical analyses demonstrate the efficiency of ShortGAC compared to other general-purpose propagation algorithms. In some cases ShortGAC even exhibits similar performance to special-purpose propagators.
Cite
Text
Nightingale et al. "Exploiting Short Supports for Generalised Arc Consistency for Arbitrary Constraints." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-111Markdown
[Nightingale et al. "Exploiting Short Supports for Generalised Arc Consistency for Arbitrary Constraints." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/nightingale2011ijcai-exploiting/) doi:10.5591/978-1-57735-516-8/IJCAI11-111BibTeX
@inproceedings{nightingale2011ijcai-exploiting,
title = {{Exploiting Short Supports for Generalised Arc Consistency for Arbitrary Constraints}},
author = {Nightingale, Peter and Gent, Ian P. and Jefferson, Christopher and Miguel, Ian},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2011},
pages = {623-628},
doi = {10.5591/978-1-57735-516-8/IJCAI11-111},
url = {https://mlanthology.org/ijcai/2011/nightingale2011ijcai-exploiting/}
}