# Online Handbook of Argumentation for AI: Volume 2

arXiv: https://arxiv.org/abs/2106.10832 · HTML: https://mlsanity.com/paper/2106.10832/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 104 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Scaling Laws for Transfer](https://mlsanity.com/paper/2102.01293/) (2021)
- [CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters](https://mlsanity.com/paper/1705.07664/) (2017)
- [A General Account of Argumentation with Preferences](https://mlsanity.com/paper/1804.06763/) (2018)
- [A Dataset Independent Set of Baselines for Relation Prediction in Argument Mining](https://mlsanity.com/paper/2003.04970/) (2020)
- [Computing Preferred Extensions in Abstract Argumentation: a SAT-based Approach](https://mlsanity.com/paper/1310.4986/) (2013)
- [Social Media Argumentation Mining: The Quest for Deliberateness in Raucousness](https://mlsanity.com/paper/1701.00168/) (2016)
- [Argument-based Belief in Topological Structures](https://mlsanity.com/paper/1707.08762/) (2017)
- [A Bayesian Approach to Direct and Inverse Abstract Argumentation Problems](https://mlsanity.com/paper/1909.04319/) (2019)

## Abstract (arXiv)

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