# Learning Brave Assumption-Based Argumentation Frameworks via ASP

arXiv: https://arxiv.org/abs/2408.10126 · HTML: https://mlsanity.com/paper/2408.10126/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [A New Algorithm to Automate Inductive Learning of Default Theories](https://mlsanity.com/paper/1707.02693/) (2017)
- [FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data](https://mlsanity.com/paper/2202.06913/) (2022)
- [ABA Learning via ASP](https://mlsanity.com/paper/2308.15877/) (2023)
- [Learning Assumption-based Argumentation Frameworks](https://mlsanity.com/paper/2305.15921/) (2023)

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

- [Object-Centric Neuro-Argumentative Learning](https://mlsanity.com/paper/2506.14577/) (2025)
- [Heterogeneous Graph Neural Networks for Assumption-Based Argumentation](https://mlsanity.com/paper/2511.08982/) (2025)
- [Constrained Assumption-Based Argumentation Frameworks](https://mlsanity.com/paper/2602.13135/) (2026)

## Abstract (arXiv)

Assumption-based Argumentation (ABA) is advocated as a unifying formalism for various forms of non-monotonic reasoning, including logic programming. It allows capturing defeasible knowledge, subject to argumentative debate. While, in much existing work, ABA frameworks are given up-front, in this paper we focus on the problem of automating their learning from background knowledge and positive/negative examples. Unlike prior work, we newly frame the problem in terms of brave reasoning under stable extensions for ABA. We present a novel algorithm based on transformation rules (such as Rote Learning, Folding, Assumption Introduction and Fact Subsumption) and an implementation thereof that makes use of Answer Set Programming. Finally, we compare our technique to state-of-the-art ILP systems that learn defeasible knowledge.