Object-Centric Neuro-Argumentative Learning
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915348470562816 |
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| author | Jacob, Abdul Rahman Kori, Avinash De Angelis, Emanuele Glocker, Ben Proietti, Maurizio Toni, Francesca |
| author_facet | Jacob, Abdul Rahman Kori, Avinash De Angelis, Emanuele Glocker, Ben Proietti, Maurizio Toni, Francesca |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14577 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Object-Centric Neuro-Argumentative Learning Jacob, Abdul Rahman Kori, Avinash De Angelis, Emanuele Glocker, Ben Proietti, Maurizio Toni, Francesca Machine Learning Artificial Intelligence 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. |
| title | Object-Centric Neuro-Argumentative Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.14577 |