# Learning Assumption-based Argumentation Frameworks

arXiv: https://arxiv.org/abs/2305.15921 · HTML: https://mlsanity.com/paper/2305.15921/

## Key facts

- Cited by 3 papers in the map (data through 7 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 2 of 2)

- [Argumentative XAI: A Survey](https://mlsanity.com/paper/2105.11266/) (2021)
- [A New Algorithm to Automate Inductive Learning of Default Theories](https://mlsanity.com/paper/1707.02693/) (2017)

## Built on by (most cited first, 3 of 3)

- [ABA Learning via ASP](https://mlsanity.com/paper/2308.15877/) (2023)
- [Learning Brave Assumption-Based Argumentation Frameworks via ASP](https://mlsanity.com/paper/2408.10126/) (2024)
- [Argumentation and Machine Learning](https://mlsanity.com/paper/2410.23724/) (2024)

## Abstract (arXiv)

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.