Abstract 
We enable aProbLog—a probabilistic logical programming approach—to reason in presence of uncertain probabilities represented as Betadistributed random variables. We achieve the same performance of stateoftheart 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 welldefined 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. 
Authors 
 Federico Cerutti (Cardiff)
 Lance Kaplan (ARL)
 Angelika Kimmig (Cardiff)
 Murat Sensoy

Date 
Sep2019 
Venue 
Annual Fall Meeting of the DAIS ITA, 2019 

Variants 
