Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of
activity, with many proposals for languages and algorithms for inference and learning. This …

Credal networks generalize Bayesian networks to allow for imprecision in probability values.
This paper reviews the main results on credal networks under strong independence, as …

Abstract Probabilistic Answer Set Programming (PASP) combines rules, facts, and
independent probabilistic facts. We argue that a very useful modeling paradigm is obtained …

Large-scale probabilistic knowledge bases are becoming increasingly important in
academia and industry. They are continuously extended with new data, powered by modern …

We present an approach to probabilistic logic programming and probabilistic argumentation
that combines elements of the L-stable semantics and the credal semantics. We derive the …

Argumentation problems are concerned with determining the acceptability of a set of
arguments from their relational structure. When the available information is uncertain …

Abstract Probabilistic Logic Programs under the distribution semantics (PLPDS) do not allow
statistical probabilistic statements of the form “90% of birds fly”, which were defined “Type 1” …

We introduce SMProbLog, a generalization of the probabilistic logic programming language
ProbLog. A ProbLog program defines a distribution over logic programs by specifying for …

Abstract Probabilistic Answer Set Programming offers an intuitive and powerful declarative
language to represent uncertainty about combinatorial structures. Remarkably, under the …

We analyze the computational complexity of probabilistic logic programming with
constraints, disjunctive heads, and aggregates such as sum and max. We consider …