Deep Learning for Abstract Argumentation Semantics

Abstract

This thesis presents a learning-based approach to determining acceptance of arguments under several abstract argumentation semantics. More specifically, this work proposes 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. This work shows how the latter can be done for multi-extension semantics by using AGNNs to guide a basic search.