Thirty-Sixth AAAI Conference on Artificial Intelligence, Date: 2022/02/22 - 2022/03/01, Location: Virtual
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence
Author:
Keywords:
Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, PROPAGATION, NETWORKS, Synth - 694980;info:eu-repo/grantAgreement/EC/H2020/694980, 1SA5520N|1SA5522N#54744191, C14/18/062#54689589
Abstract:
An issue that has so far received only limited attention in probabilistic logic programming (PLP) is the modeling 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.