# Online Handbook of Argumentation for AI: Volume 4

arXiv: https://arxiv.org/abs/2401.09444 · HTML: https://mlsanity.com/paper/2401.09444/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 114 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Causal Inference and Causal Explanation with Background Knowledge](https://mlsanity.com/paper/1302.4972/) (2013)
- [Learning Equivalence Classes of Bayesian Networks Structures](https://mlsanity.com/paper/1302.3566/) (2013)
- [Argumentative XAI: A Survey](https://mlsanity.com/paper/2105.11266/) (2021)
- [CASTLE: Regularization via Auxiliary Causal Graph Discovery](https://mlsanity.com/paper/2009.13180/) (2020)
- [Combining Experts' Causal Judgments](https://mlsanity.com/paper/2005.10180/) (2020)
- [Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)](https://mlsanity.com/paper/2205.09787/) (2022)
- [Weakly Complete Semantics Based on Undecidedness Blocking](https://mlsanity.com/paper/2103.10701/) (2021)
- [Stability in Abstract Argumentation](https://mlsanity.com/paper/2012.12588/) (2020)
- [Online Handbook of Argumentation for AI: Volume 1](https://mlsanity.com/paper/2006.12020/) (2020)
- [Revisiting initial sets in abstract argumentation](https://mlsanity.com/paper/2204.09985/) (2022)
- [Online Handbook of Argumentation for AI: Volume 3](https://mlsanity.com/paper/2212.07996/) (2022)

## Abstract (arXiv)

This volume contains revised versions of the papers selected for the fourth 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.