  This paper studies a fundamental mechanism of how to detect a conflict
between arguments given sentiments regarding acceptability of the arguments. We
introduce a concept of the inverse problem of the abstract argumentation to
tackle the problem. Given noisy sets of acceptable arguments, it aims to find
attack relations explaining the sets well in terms of acceptability semantics.
It is the inverse of the direct problem corresponding to the traditional
problem of the abstract argumentation that focuses on finding sets of
acceptable arguments in terms of the semantics given an attack relation between
the arguments. We give a probabilistic model handling both of the problems in a
way that is faithful to the acceptability semantics. From a theoretical point
of view, we show that a solution to both the direct and inverse problems is a
special case of the probabilistic inference on the model. We discuss that the
model provides a natural extension of the semantics to cope with uncertain
attack relations distributed probabilistically. From en empirical point of
view, we argue that it reasonably predicts individuals sentiments regarding
acceptability of arguments. This paper contributes to lay the foundation for
making acceptability semantics data-driven and to provide a way to tackle the
knowledge acquisition bottleneck.

Artificial Intelligence;Machine Learning;Logic in Computer Science