Inference and Learning with Model Uncertainty in Probabilistic Logic Programs
Victor Verreet, Vincent Derkinderen, Pedro Zuidberg Dos Martires, Luc De Raedt
Abstract
An issue that has so far received only limited attention in probabilistic logic programming (PLP) is the modelling of so-called epistemic uncertainty, the uncertainty about the model itself. Accurately quantifying this model uncertainty is paramount to robust inference, learning and ultimately decision making. We introduce BetaProbLog, a PLP language that can model epistemic uncertainty. BetaProbLog has sound semantics, an effective inference algorithm that combines Monte Carlo techniques with knowledge compilation, and a parameter learning algorithm. We empirically outperform state-of-the-art methods on probabilistic inference tasks in second-order Bayesian networks, digit classification and discriminative learning in the presence of epistemic uncertainty.
BibTeX
@inproceedings{aaai2022_inferenceandlear,
title = {Inference and Learning with Model Uncertainty in Probabilistic Logic Programs},
author = {Victor Verreet and Vincent Derkinderen and Pedro Zuidberg Dos Martires and Luc De Raedt},
booktitle = {AAAI 2022},
year = {2022}
}