TY - JOUR
AU - Cerutti, Federico
AU - Kaplan, Lance
AU - Kimmig, Angelika
AU - Şensoy, Murat
PY - 2019/07/17
Y2 - 2022/05/17
TI - Probabilistic Logic Programming with Beta-Distributed Random Variables
JF - Proceedings of the AAAI Conference on Artificial Intelligence
JA - AAAI
VL - 33
IS - 01
SE - AAAI Technical Track: Reasoning under Uncertainty
DO - 10.1609/aaai.v33i01.33017769
UR - https://ojs.aaai.org/index.php/AAAI/article/view/4773
SP - 7769-7776
AB - <p>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.</p>
ER -