  Argumentation problems are concerned with determining the acceptability of a
set of arguments from their relational structure. When the available
information is uncertain, probabilistic argumentation frameworks provide
modelling tools to account for it. The first contribution of this paper is a
novel interpretation of probabilistic argumentation frameworks as probabilistic
logic programs. Probabilistic logic programs are logic programs in which some
of the facts are annotated with probabilities. We show that the programs
representing probabilistic argumentation frameworks do not satisfy a common
assumption in probabilistic logic programming (PLP) semantics, which is, that
probabilistic facts fully capture the uncertainty in the domain under
investigation. The second contribution of this paper is then a novel PLP
semantics for programs where a choice of probabilistic facts does not uniquely
determine the truth assignment of the logical atoms. The third contribution of
this paper is the implementation of a PLP system supporting this semantics:
smProbLog. smProbLog is a novel PLP framework based on the probabilistic logic
programming language ProbLog. smProbLog supports many inference and learning
tasks typical of PLP, which, together with our first contribution, provide
novel reasoning tools for probabilistic argumentation. We evaluate our approach
with experiments analyzing the computational cost of the proposed algorithms
and their application to a dataset of argumentation problems.
