Object-Centric Case-Based Reasoning via Argumentation
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916981716811776 |
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| author | Gaul, Gabriel de Olim Gould, Adam Kori, Avinash Toni, Francesca |
| author_facet | Gaul, Gabriel de Olim Gould, Adam Kori, Avinash Toni, Francesca |
| contents | We introduce Slot Attention Argumentation for Case-Based Reasoning (SAA-CBR), a novel neuro-symbolic pipeline for image classification that integrates object-centric learning via a neural Slot Attention (SA) component with symbolic reasoning conducted by Abstract Argumentation for Case-Based Reasoning (AA-CBR). We explore novel integrations of AA-CBR with the neural component, including feature combination strategies, casebase reduction via representative samples, novel count-based partial orders, a One-Vs-Rest strategy for extending AA-CBR to multi-class classification, and an application of Supported AA-CBR, a bipolar variant of AA-CBR. We demonstrate that SAA-CBR is an effective classifier on the CLEVR-Hans datasets, showing competitive performance against baseline models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Object-Centric Case-Based Reasoning via Argumentation Gaul, Gabriel de Olim Gould, Adam Kori, Avinash Toni, Francesca Artificial Intelligence We introduce Slot Attention Argumentation for Case-Based Reasoning (SAA-CBR), a novel neuro-symbolic pipeline for image classification that integrates object-centric learning via a neural Slot Attention (SA) component with symbolic reasoning conducted by Abstract Argumentation for Case-Based Reasoning (AA-CBR). We explore novel integrations of AA-CBR with the neural component, including feature combination strategies, casebase reduction via representative samples, novel count-based partial orders, a One-Vs-Rest strategy for extending AA-CBR to multi-class classification, and an application of Supported AA-CBR, a bipolar variant of AA-CBR. We demonstrate that SAA-CBR is an effective classifier on the CLEVR-Hans datasets, showing competitive performance against baseline models. |
| title | Object-Centric Case-Based Reasoning via Argumentation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.00185 |