  In this paper, we present a learning-based approach to determining acceptance
of arguments under several abstract argumentation semantics. More specifically,
we propose an argumentation graph neural network (AGNN) that learns a
message-passing algorithm to predict the likelihood of an argument being
accepted. The experimental results demonstrate that the AGNN can almost
perfectly predict the acceptability under different semantics and scales well
for larger argumentation frameworks. Furthermore, analysing the behaviour of
the message-passing algorithm shows that the AGNN learns to adhere to basic
principles of argument semantics as identified in the literature, and can thus
be trained to predict extensions under the different semantics - we show how
the latter can be done for multi-extension semantics by using AGNNs to guide a
basic search. We publish our code at
https://github.com/DennisCraandijk/DL-Abstract-Argumentation
