Inference and Learning with Model Uncertainty in Probabilistic Logic Programs

Authors

  • Victor Verreet Department of Computer Science, KU Leuven, Belgium Leuven.AI - KU Leuven Institute for AI, Belgium
  • Vincent Derkinderen Department of Computer Science, KU Leuven, Belgium Leuven.AI - KU Leuven Institute for AI, Belgium
  • Pedro Zuidberg Dos Martires Department of Computer Science, KU Leuven, Belgium Leuven.AI - KU Leuven Institute for AI, Belgium
  • Luc De Raedt Department of Computer Science, KU Leuven, Belgium Leuven.AI - KU Leuven Institute for AI, Belgium Center for Applied Autonomous Systems, Orebro University, Sweden

DOI:

https://doi.org/10.1609/aaai.v36i9.21245

Keywords:

Reasoning Under Uncertainty (RU)

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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Published

2022-06-28

How to Cite

Verreet, V., Derkinderen, V., Martires, P. Z. D., & Raedt, L. D. (2022). Inference and Learning with Model Uncertainty in Probabilistic Logic Programs. Proceedings of the AAAI Conference on Artificial Intelligence, 36(9), 10060-10069. https://doi.org/10.1609/aaai.v36i9.21245

Issue

Section

AAAI Technical Track on Reasoning under Uncertainty