Learning Assumption-based Argumentation Frameworks

TL;DR

This paper introduces a new method for learning assumption-based argumentation frameworks from data, using transformation rules to handle both stratified and non-stratified cases, improving interpretability and flexibility.

Contribution

It presents a novel learning approach that interprets exceptions as undercutting attacks and employs transformation rules, including folding, to learn complex argumentation frameworks.

Findings

Successfully reconstructs existing logic-based learning approaches

Handles non-stratified argumentation frameworks effectively

Simplifies learning of complex, hard problems

Abstract

We propose a novel approach to logic-based learning which generates assumption-based argumentation (ABA) frameworks from positive and negative examples, using a given background knowledge. These ABA frameworks can be mapped onto logic programs with negation as failure that may be non-stratified. Whereas existing argumentation-based methods learn exceptions to general rules by interpreting the exceptions as rebuttal attacks, our approach interprets them as undercutting attacks. Our learning technique is based on the use of transformation rules, including some adapted from logic program transformation rules (notably folding) as well as others, such as rote learning and assumption introduction. We present a general strategy that applies the transformation rules in a suitable order to learn stratified frameworks, and we also propose a variant that handles the non-stratified case. We illustrate the benefits of our approach with a number of examples, which show that, on one hand, we are able to easily reconstruct other logic-based learning approaches and, on the other hand, we can work out in a very simple and natural way problems that seem to be hard for existing techniques.