Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints Using Discrete Fourier Transform
Artificial Intelligence
2020-07-17 v2 Logic in Computer Science
Abstract
In this paper we show that inference in 2-variable Markov logic networks (MLNs) with cardinality and function constraints is domain-liftable. To obtain this result we use existing domain-lifted algorithms for weighted first-order model counting (Van den Broeck et al, KR 2014) together with discrete Fourier transform of certain distributions associated to MLNs.
Cite
@article{arxiv.2006.03432,
title = {Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints Using Discrete Fourier Transform},
author = {Ondrej Kuzelka},
journal= {arXiv preprint arXiv:2006.03432},
year = {2020}
}
Comments
arXiv admin note: text overlap with arXiv:2002.10259, This version: fixed a typo in Section 3.1