We examine the meaning and the complexity of probabilistic logic programs that consist of a
set of rules and a set of independent probabilistic facts (that is, programs based on Sato's
distribution semantics). We focus on two semantics, respectively based on stable and on
well-founded models. We show that the semantics based on stable models (referred to as
the" credal semantics") produces sets of probability measures that dominate infinitely
monotone Choquet capacities; we describe several useful consequences of this result. We …

We examine the meaning and the complexity of probabilistic logic programs that consist of a
set of rules and a set of independent probabilistic facts (that is, programs based on Sato's
distribution semantics). We focus on two semantics, respectively based on stable and on
well-founded models. We show that the semantics based on stable models (referred to as
the" credal semantics") produces sets of probability models that dominate infinitely
monotone Choquet capacities, we describe several useful consequences of this result. We …