ABA Learning via ASP

TL;DR

This paper introduces a novel approach to ABA Learning by leveraging Answer Set Programming, aiming to enhance the process of deriving argumentation frameworks from background knowledge and examples.

Contribution

It presents a new method for implementing ABA Learning with ASP, improving guidance for rote learning and generalization in argumentation frameworks.

Findings

ASP-based ABA Learning effectively derives argumentation frameworks.

The method improves guidance in rote learning and generalization.

Potential for enhanced symbolic machine learning applications.

Abstract

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.