# Online Handbook of Argumentation for AI: Volume 3

arXiv: https://arxiv.org/abs/2212.07996 · HTML: https://mlsanity.com/paper/2212.07996/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 92 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A General Account of Argumentation with Preferences](https://mlsanity.com/paper/1804.06763/) (2018)
- [Deep Learning for Abstract Argumentation Semantics](https://mlsanity.com/paper/2007.07629/) (2020)
- [Explainability in autonomous pedagogically structured scenarios](https://mlsanity.com/paper/2210.12140/) (2022)
- [A Labelling Framework for Probabilistic Argumentation](https://mlsanity.com/paper/1708.00109/) (2017)
- [Value of Information for Argumentation based Intelligence Analysis](https://mlsanity.com/paper/2102.08180/) (2021)

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

- [Online Handbook of Argumentation for AI: Volume 4](https://mlsanity.com/paper/2401.09444/) (2023)

## Abstract (arXiv)

This volume contains revised versions of the papers selected for the third volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.