Object-centric case-based reasoning via argumentation
File(s)
Published version
Author(s)
De Olim Gaul, G
Gould, A
Kori, A
Toni, F
Type
Conference Paper
Abstract
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.
Date Issued
2025-10-16
Date Acceptance
2025-10-01
Citation
Ceur Workshop Proceedings, 2025, 4066, pp.36-49
ISSN
1613-0073
Publisher
CEUR-WS.org
Start Page
36
End Page
49
Journal / Book Title
Ceur Workshop Proceedings
Volume
4066
Copyright Statement
© 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
License URL
Source
3rd International Workshop on Argumentation for eXplainable AI (ArgXAI 2025)
Subjects
Computational Argumentation
Slot Attention
Case-Based Reasoning
Neuro-Symbolic AI
Publication Status
Published
Start Date
2025-10-26
Coverage Spatial
Bologna, Italy