Quadratization and Roof Duality of Markov Logic Networks
Abstract
This article discusses the quadratization of Markov Logic Networks, which enables efficient approximate MAP computation by means of maximum ows. The procedure relies on a pseudo-Boolean representation of the model, and allows handling models of any order. The employed pseudo-Boolean representation can be used to identify problems that are guaranteed to be solvable in low polynomial-time. Results on common benchmark problems show that the proposed approach finds optimal assignments for most variables in excellent computational time and approximate solutions that match the quality of ILP-based solvers.
Cite
Text
de Nijs et al. "Quadratization and Roof Duality of Markov Logic Networks." Journal of Artificial Intelligence Research, 2016. doi:10.1613/JAIR.5023Markdown
[de Nijs et al. "Quadratization and Roof Duality of Markov Logic Networks." Journal of Artificial Intelligence Research, 2016.](https://mlanthology.org/jair/2016/denijs2016jair-quadratization/) doi:10.1613/JAIR.5023BibTeX
@article{denijs2016jair-quadratization,
title = {{Quadratization and Roof Duality of Markov Logic Networks}},
author = {de Nijs, Roderick and Landsiedel, Christian and Wollherr, Dirk and Buss, Martin},
journal = {Journal of Artificial Intelligence Research},
year = {2016},
pages = {685-714},
doi = {10.1613/JAIR.5023},
volume = {55},
url = {https://mlanthology.org/jair/2016/denijs2016jair-quadratization/}
}