# Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation

arXiv: https://arxiv.org/abs/2108.04192 · HTML: https://mlsanity.com/paper/2108.04192/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [How to build your own ASP-based system?!](https://mlsanity.com/paper/2008.06692/) (2020)
- [ABA+: Assumption-Based Argumentation with Preferences](https://mlsanity.com/paper/1610.03024/) (2016)
- [Properties of ABA+ for Non-Monotonic Reasoning](https://mlsanity.com/paper/1603.08714/) (2016)
- [Resolving Conflicts in Clinical Guidelines using Argumentation](https://mlsanity.com/paper/1902.07526/) (2019)

## Built on by (most cited first, 4 of 4)

- [Instantiations and Computational Aspects of Non-Flat Assumption-based Argumentation](https://mlsanity.com/paper/2404.11431/) (2024)
- [On Gradual Semantics for Assumption-Based Argumentation](https://mlsanity.com/paper/2507.10076/) (2025)
- [On Strong and Weak Admissibility in Non-Flat Assumption-Based Argumentation](https://mlsanity.com/paper/2508.11182/) (2025)
- [Splitting Assumption-Based Argumentation Frameworks](https://mlsanity.com/paper/2604.27964/) (2026)

## Abstract (arXiv)

Assumption-based argumentation (ABA) is a central structured argumentation formalism. As shown recently, answer set programming (ASP) enables efficiently solving NP-hard reasoning tasks of ABA in practice, in particular in the commonly studied logic programming fragment of ABA. In this work, we harness recent advances in incremental ASP solving for developing effective algorithms for reasoning tasks in the logic programming fragment of ABA that are presumably hard for the second level of the polynomial hierarchy, including skeptical reasoning under preferred semantics as well as preferential reasoning. In particular, we develop non-trivial counterexample-guided abstraction refinement procedures based on incremental ASP solving for these tasks. We also show empirically that the procedures are significantly more effective than previously proposed algorithms for the tasks. This paper is under consideration for acceptance in TPLP.