Inference and Learning with Model Uncertainty in Probabilistic Logic Programs

Victor Verreet, Vincent Derkinderen, Pedro Zuidberg Dos Martires, Luc De Raedt

AAAI 2022 pp. 10060-10069

doi:10.1609/AAAI.V36I9.21245 /aaai/2022/verreet2022aaai-inference/

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.

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Text

Verreet et al. "Inference and Learning with Model Uncertainty in Probabilistic Logic Programs." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I9.21245

Markdown

[Verreet et al. "Inference and Learning with Model Uncertainty in Probabilistic Logic Programs." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/verreet2022aaai-inference/) doi:10.1609/AAAI.V36I9.21245

BibTeX

@inproceedings{verreet2022aaai-inference,
  title     = {{Inference and Learning with Model Uncertainty in Probabilistic Logic Programs}},
  author    = {Verreet, Victor and Derkinderen, Vincent and Dos Martires, Pedro Zuidberg and De Raedt, Luc},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {10060-10069},
  doi       = {10.1609/AAAI.V36I9.21245},
  url       = {https://mlanthology.org/aaai/2022/verreet2022aaai-inference/}
}