Inference and Learning with Model Uncertainty in Probabilistic Logic Programs

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.

Authors

• Victor Verreet
• Vincent Derkinderen KU Leuven
• Pedro Zuidberg Dos Martires Örebro University
• Luc De Raedt KU Leuven Örebro University

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