An A-Star Algorithm for Argumentative Rule Extraction

Benoît Alcaraz, Adam Kaliski, Christopher Leturc

2025

Abstract

In this paper, we present an approach for inferring logical rules in the form of formal argumentation frameworks using the A∗algorithm. We show that contextual argumentation frameworks — in which arguments are activated and deactivated based on the values of the boolean variables that the arguments represent — allow for a concise, graphical, and hence explainable representation of logical rules. We define the proposed approach as a tool to understand the behaviour of already deployed black-box agents. Additionally, we show several applications where having an argumentation framework representing an agent decision’s model is required or could be beneficial. We then apply our algorithm to several datasets in order to evaluate its performances. The algorithm reaches high accuracy scores on discrete datasets, indicating that our approach could be a promising avenue for alternative data-driven AI learning techniques, especially in the context of explainable AI.

Paper Citation

in Harvard Style

Alcaraz B., Kaliski A. and Leturc C. (2025). An A-Star Algorithm for Argumentative Rule Extraction. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 91-101. DOI: 10.5220/0013110400003890

in Bibtex Style

@conference{icaart25,
author={Benoît Alcaraz and Adam Kaliski and Christopher Leturc},
title={An A-Star Algorithm for Argumentative Rule Extraction},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={91-101},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013110400003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - An A-Star Algorithm for Argumentative Rule Extraction
SN - 978-989-758-737-5
AU - Alcaraz B.
AU - Kaliski A.
AU - Leturc C.
PY - 2025
SP - 91
EP - 101
DO - 10.5220/0013110400003890
PB - SciTePress