Probabilistic Logic Programming with Beta-Distributed Random Variables

  • Federico Cerutti Cardiff University
  • Lance Kaplan U.S. Army Research Laboratory
  • Angelika Kimmig Cardiff University
  • Murat Şensoy Ozyegin University

Abstract

We enable aProbLog—a probabilistic logical programming approach—to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms for highly specified and engineered domains, while simultaneously we maintain the flexibility offered by aProbLog in handling complex relational domains. Our motivation is that faithfully capturing the distribution of probabilities is necessary to compute an expected utility for effective decision making under uncertainty: unfortunately, these probability distributions can be highly uncertain due to sparse data. To understand and accurately manipulate such probability distributions we need a well-defined theoretical framework that is provided by the Beta distribution, which specifies a distribution of probabilities representing all the possible values of a probability when the exact value is unknown.

Published
2019-07-17
Section
AAAI Technical Track: Reasoning under Uncertainty