Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)
Artificial Intelligence
2016-11-21 v1
Abstract
In this paper, we advocate the use of stratified logical theories for representing probabilistic models. We argue that such encodings can be more interpretable than those obtained in existing frameworks such as Markov logic networks. Among others, this allows for the use of domain experts to improve learned models by directly removing, adding, or modifying logical formulas.
Cite
@article{arxiv.1611.06174,
title = {Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)},
author = {Ondrej Kuzelka and Jesse Davis and Steven Schockaert},
journal= {arXiv preprint arXiv:1611.06174},
year = {2016}
}
Comments
Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems