From Weighted Conditionals of Multilayer Perceptrons to Gradual
  Argumentation and Back
  A fuzzy multipreference semantics has been recently proposed for weighted
conditional knowledge bases, and used to develop a logical semantics for
Multilayer Perceptrons, by regarding a deep neural network (after training) as
a weighted conditional knowledge base. This semantics, in its different
variants, suggests some gradual argumentation semantics, which are related to
the family of the gradual semantics studied by Amgoud and Doder. The
relationships between weighted conditional knowledge bases and MLPs extend to
the proposed gradual semantics to capture the stationary states of MPs, in
agreement with previous results on the relationship between argumentation
frameworks and neural networks. The paper also suggests a simple way to extend
the proposed semantics to deal attacks/supports by a boolean combination of
arguments, based on the fuzzy semantics of weighted conditionals, as well as an
approach for defeasible reasoning over a weighted argumentation graph, building
on the proposed gradual semantics.
