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 …

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 …

Completion of large-scale knowledge bases, such as DBPedia or Freebase, often relies on
embedding models that turn symbolic relations into vector-based representations. Such …

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 …

Abstract Probabilistic Answer Set Programming under the credal semantics (PASP)
describes an uncertain domain through an answer set program extended with probabilistic …

Learning from 1-step transitions (LF1T) has become a paradigm to construct a logical
hypothesis of a dynamic system, such as a Boolean network, from its synchronized state …

Extending programming languages with stochastic behaviour such as probabilistic choices
or random sampling has a long tradition in computer science. A recent development in this …