ABA Learning via ASP
This work addresses the challenge of improving ABA Learning for symbolic reasoning tasks, but it appears incremental as it builds on existing ABA Learning methods.
The authors tackled the problem of implementing Assumption-Based Argumentation (ABA) Learning, a symbolic machine learning approach, by proposing a novel method using Answer Set Programming (ASP) to guide rote learning and generalization.
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.