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arXiv · 2506.14577

Object-Centric Neuro-Argumentative Learning

Abdul Rahman Jacob·Avinash Kori·Emanuele De Angelis·Ben Glocker·Maurizio Proietti·Francesca Toni
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Abstract

Over the last decade, as we rely more on deep learning technologies to make critical decisions, concerns regarding their safety, reliability and interpretability have emerged. We introduce a novel Neural Argumentative Learning (NAL) architecture that integrates Assumption-Based Argumentation (ABA) with deep learning for image analysis. Our architecture consists of neural and symbolic components. The former segments and encodes images into facts using object-centric learning, while the latter applies ABA learning to develop ABA frameworks enabling predictions with images. Experiments on synthetic data show that the NAL architecture can be competitive with a state-of-the-art alternative.

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BibTeXRIS

Abdul Rahman Jacob, Avinash Kori, Emanuele De Angelis, Ben Glocker, Maurizio Proietti, Francesca Toni. 2025-06-17. Object-Centric Neuro-Argumentative Learning. https://arxiv.org/abs/2506.14577

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