  We show that an interesting class of feed-forward neural networks can be
understood as quantitative argumentation frameworks. This connection creates a
bridge between research in Formal Argumentation and Machine Learning. We
generalize the semantics of feed-forward neural networks to acyclic graphs and
study the resulting computational and semantical properties in argumentation
graphs. As it turns out, the semantics gives stronger guarantees than existing
semantics that have been tailor-made for the argumentation setting. From a
machine-learning perspective, the connection does not seem immediately helpful.
While it gives intuitive meaning to some feed-forward-neural networks, they
remain difficult to understand due to their size and density. However, the
connection seems helpful for combining background knowledge in form of sparse
argumentation networks with dense neural networks that have been trained for
complementary purposes and for learning the parameters of quantitative
argumentation frameworks in an end-to-end fashion from data.
