Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas
Abstract
Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint (also called a CSP) is perhaps the most significant—being the most well-studied and has the ability to encode any other constraints defined on finite variables. Thus, designing efficient filtering algorithms on table constraints has attracted significant research efforts. In turn, there have been great improvements in efficiency over time with the evolution and development of AC and GAC algorithms. In this paper, we survey the existing filtering algorithms for table constraint focusing on historically important ideas and recent successful techniques shown to be effective.
Cite
Text
Yap et al. "Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I09.7086Markdown
[Yap et al. "Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/yap2020aaai-generalized/) doi:10.1609/AAAI.V34I09.7086BibTeX
@inproceedings{yap2020aaai-generalized,
title = {{Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas}},
author = {Yap, Roland H. C. and Xia, Wei and Wang, Ruiwei},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {13590-13597},
doi = {10.1609/AAAI.V34I09.7086},
url = {https://mlanthology.org/aaai/2020/yap2020aaai-generalized/}
}