Dung’s Argumentation Framework: Unveiling the Expressive Power with Inconsistent Databases

Authors

  • Yasir Mahmood DICE group, Department of Computer Science, Paderborn University, Germany
  • Markus Hecher Univ. Artois, CNRS, UMR 8188, Centre de Recherche en Informatique de Lens (CRIL), F-62300 Lens, France CSAIL, Massachusetts Institute of Technology, United States
  • Axel-Cyrille Ngonga Ngomo DICE group, Department of Computer Science, Paderborn University, Germany

DOI:

https://doi.org/10.1609/aaai.v39i14.33651

Abstract

The connection between inconsistent databases and Dung’s abstract argumentation framework has recently drawn growing interest. Specifically, an inconsistent database, involving certain types of integrity constraints such as functional and inclusion dependencies, can be viewed as an argumentation framework in Dung’s setting. Nevertheless, no prior work has explored the exact expressive power of Dung’s theory of argumentation when compared to inconsistent databases and integrity constraints. In this paper, we close this gap by arguing that an argumentation framework can also be viewed as an inconsistent database. We first establish a connection between subset-repairs for databases and extensions for AFs considering conflict-free, naive, admissible, and preferred semantics. Further, we define a new family of attribute-based repairs based on the principle of maximal content preservation. The effectiveness of these repairs is then highlighted by connecting them to stable, semi-stable, and stage semantics. Our main contributions include translating an argumentation framework into a database together with integrity constraints. Moreover, this translation can be achieved in polynomial time, which is essential in transferring complexity results between the two formalisms.

Published

2025-04-11

How to Cite

Mahmood, Y., Hecher, M., & Ngonga Ngomo, A.-C. (2025). Dung’s Argumentation Framework: Unveiling the Expressive Power with Inconsistent Databases. Proceedings of the AAAI Conference on Artificial Intelligence, 39(14), 15058–15066. https://doi.org/10.1609/aaai.v39i14.33651

Issue

Section

AAAI Technical Track on Knowledge Representation and Reasoning