A Propagator Design Framework for Constraints over Sequences
Abstract
Constraints over variable sequences are ubiquitous and many of their propagators have been inspired by dynamic programming (DP). We propose a conceptual framework for designing such propagators: pruning rules, in a functional notation, are refined upon the application of transformation operators to a DP-style formulation of a constraint; a representation of the (tuple) variable domains is picked; and a control of the pruning rules is picked.
Cite
Text
Monette et al. "A Propagator Design Framework for Constraints over Sequences." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.9113Markdown
[Monette et al. "A Propagator Design Framework for Constraints over Sequences." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/monette2014aaai-propagator/) doi:10.1609/AAAI.V28I1.9113BibTeX
@inproceedings{monette2014aaai-propagator,
title = {{A Propagator Design Framework for Constraints over Sequences}},
author = {Monette, Jean-Noël and Flener, Pierre and Pearson, Justin},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2014},
pages = {2710-2716},
doi = {10.1609/AAAI.V28I1.9113},
url = {https://mlanthology.org/aaai/2014/monette2014aaai-propagator/}
}