# A Unifying Framework for Learning Argumentation Semantics

arXiv: https://arxiv.org/abs/2310.12309 · HTML: https://mlsanity.com/paper/2310.12309/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 18 Oct 2023.

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

- [Inductive Learning of Answer Set Programs from Noisy Examples](https://mlsanity.com/paper/1808.08441/) (2018)
- [Deep Learning for Abstract Argumentation Semantics](https://mlsanity.com/paper/2007.07629/) (2020)

## Built on by (most cited first, 1 of 1)

- [Enhancing Conflict Resolution in Language Models via Abstract Argumentation](https://mlsanity.com/paper/2412.16725/) (2024)

## Abstract (arXiv)

Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artificial agents. Acceptability semantics of formal argumentation systems define the criteria for the acceptance or rejection of arguments. Several software systems, known as argumentation solvers, have been developed to compute the accepted/rejected arguments using such criteria. These include systems that learn to identify the accepted arguments using non-interpretable methods. In this paper we present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Through an empirical evaluation we show that our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.