Emanuele De Angelis IASI-CNR, Rome, Italy, Maurizio Proietti IASI-CNR, Rome, Italy, Francesca Toni Department of Computing, Imperial College London, UK

Recently, ABA Learning has been proposed as a form of symbolic machine learning for drawing Assumption-Based Argumentation frameworks from background knowledge and positive and negative examples. We propose a novel method for implementing ABA Learning using Answer Set Programming as a way to help guide Rote Learning and generalisation in ABA Learning.