# Grounding Rule-Based Argumentation Using Datalog

arXiv: https://arxiv.org/abs/2508.10976 · HTML: https://mlsanity.com/paper/2508.10976/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 14 Aug 2025.

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

- [Advancing Lazy-Grounding ASP Solving Techniques -- Restarts, Phase Saving, Heuristics, and More](https://mlsanity.com/paper/2008.03526/) (2020)
- [ABA+: Assumption-Based Argumentation with Preferences](https://mlsanity.com/paper/1610.03024/) (2016)
- [Column-Oriented Datalog Materialization for Large Knowledge Graphs (Extended Technical Report)](https://mlsanity.com/paper/1511.08915/) (2015)
- [Instantiations and Computational Aspects of Non-Flat Assumption-based Argumentation](https://mlsanity.com/paper/2404.11431/) (2024)
- [The Design of the Fifth Answer Set Programming Competition](https://mlsanity.com/paper/1405.3710/) (2014)
- [A Comparative Study of Some Central Notions of ASPIC+ and DeLP](https://mlsanity.com/paper/1909.02810/) (2019)

## Abstract (arXiv)

ASPIC+ is one of the main general frameworks for rule-based argumentation for AI. Although first-order rules are commonly used in ASPIC+ examples, most existing approaches to reason over rule-based argumentation only support propositional rules. To enable reasoning over first-order instances, a preliminary grounding step is required. As groundings can lead to an exponential increase in the size of the input theories, intelligent procedures are needed. However, there is a lack of dedicated solutions for ASPIC+. Therefore, we propose an intelligent grounding procedure that keeps the size of the grounding manageable while preserving the correctness of the reasoning process. To this end, we translate the first-order ASPIC+ instance into a Datalog program and query a Datalog engine to obtain ground substitutions to perform the grounding of rules and contraries. Additionally, we propose simplifications specific to the ASPIC+ formalism to avoid grounding of rules that have no influence on the reasoning process. Finally, we performed an empirical evaluation of a prototypical implementation to show scalability.