# Instantiations and Computational Aspects of Non-Flat Assumption-based Argumentation

arXiv: https://arxiv.org/abs/2404.11431 · HTML: https://mlsanity.com/paper/2404.11431/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 17 Apr 2024.

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

- [Argumentative XAI: A Survey](https://mlsanity.com/paper/2105.11266/) (2021)
- [A General Account of Argumentation with Preferences](https://mlsanity.com/paper/1804.06763/) (2018)
- [Design and Results of the Second International Competition on Computational Models of Argumentation](https://mlsanity.com/paper/1909.00621/) (2019)
- [Non-flat ABA is an Instance of Bipolar Argumentation](https://mlsanity.com/paper/2305.12453/) (2023)
- [Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation](https://mlsanity.com/paper/2108.04192/) (2021)
- [Complexity Results and Algorithms for Bipolar Argumentation](https://mlsanity.com/paper/1903.01964/) (2019)

## Built on by (most cited first, 7 of 7)

- [Argumentative Causal Discovery](https://mlsanity.com/paper/2405.11250/) (2024)
- [On Gradual Semantics for Assumption-Based Argumentation](https://mlsanity.com/paper/2507.10076/) (2025)
- [On the Correspondence of Non-flat Assumption-based Argumentation and Logic Programming with Negation as Failure in the Head](https://mlsanity.com/paper/2405.09415/) (2024)
- [Dialogue-based Explanations for Logical Reasoning using Structured Argumentation](https://mlsanity.com/paper/2502.11291/) (2025)
- [Heterogeneous Graph Neural Networks for Assumption-Based Argumentation](https://mlsanity.com/paper/2511.08982/) (2025)
- [Grounding Rule-Based Argumentation Using Datalog](https://mlsanity.com/paper/2508.10976/) (2025)
- [Splitting Assumption-Based Argumentation Frameworks](https://mlsanity.com/paper/2604.27964/) (2026)

## Abstract (arXiv)

Most existing computational tools for assumption-based argumentation (ABA) focus on so-called flat frameworks, disregarding the more general case. In this paper, we study an instantiation-based approach for reasoning in possibly non-flat ABA. We make use of a semantics-preserving translation between ABA and bipolar argumentation frameworks (BAFs). By utilizing compilability theory, we establish that the constructed BAFs will in general be of exponential size. In order to keep the number of arguments and computational cost low, we present three ways of identifying redundant arguments. Moreover, we identify fragments of ABA which admit a poly-sized instantiation. We propose two algorithmic approaches for reasoning in possibly non-flat ABA. The first approach utilizes the BAF instantiation while the second works directly without constructing arguments. An empirical evaluation shows that the former outperforms the latter on many instances, reflecting the lower complexity of BAF reasoning. This result is in contrast to flat ABA, where direct approaches dominate instantiation-based approaches.