# Deep Learning for Abstract Argumentation Semantics

arXiv: https://arxiv.org/abs/2007.07629 · HTML: https://mlsanity.com/paper/2007.07629/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [Neuro-Symbolic Artificial Intelligence: Current Trends](https://mlsanity.com/paper/2105.05330/) (2021)
- [Automatic Debate Evaluation with Argumentation Semantics and Natural Language Argument Graph Networks](https://mlsanity.com/paper/2203.14647/) (2022)
- [Enhancing Conflict Resolution in Language Models via Abstract Argumentation](https://mlsanity.com/paper/2412.16725/) (2024)
- [Online Handbook of Argumentation for AI: Volume 3](https://mlsanity.com/paper/2212.07996/) (2022)
- [A Unifying Framework for Learning Argumentation Semantics](https://mlsanity.com/paper/2310.12309/) (2023)
- [Argumentation and Machine Learning](https://mlsanity.com/paper/2410.23724/) (2024)
- [Learning Gradual Argumentation Frameworks using Genetic Algorithms](https://mlsanity.com/paper/2106.13585/) (2021)
- [Graph Convolutional Networks and Graph Attention Networks for Approximating Arguments Acceptability -- Technical Report](https://mlsanity.com/paper/2404.18672/) (2024)

## Abstract (arXiv)

In this paper, we present a learning-based approach to determining acceptance of arguments under several abstract argumentation semantics. More specifically, we propose an argumentation graph neural network (AGNN) that learns a message-passing algorithm to predict the likelihood of an argument being accepted. The experimental results demonstrate that the AGNN can almost perfectly predict the acceptability under different semantics and scales well for larger argumentation frameworks. Furthermore, analysing the behaviour of the message-passing algorithm shows that the AGNN learns to adhere to basic principles of argument semantics as identified in the literature, and can thus be trained to predict extensions under the different semantics - we show how the latter can be done for multi-extension semantics by using AGNNs to guide a basic search. We publish our code at https://github.com/DennisCraandijk/DL-Abstract-Argumentation