Neuro-argumentative learning with case-based reasoning
File(s)
Published version
Author(s)
Gould, Adam
Toni, Francesca
Type
Conference Paper
Abstract
We introduce Gradual Abstract Argumentation for Case-Based Reasoning (Gradual AA-CBR), a data-driven, neurosymbolic classification model in which the outcome is determined by an argumentation debate structure that is learned simultaneously with neural-based feature extractors. Each argument in the debate is an observed case from the training data, favouring their labelling. Cases attack or support those with opposing or agreeing labellings, with the strength of each argument and relationship learned through gradient-based methods. This argumentation debate structure provides human-aligned reasoning, improving model interpretability compared to traditional neural networks (NNs). Unlike the existing purely symbolic variant, Abstract Argumentation for Case-Based Reasoning (AA-CBR), Gradual AA-CBR is capable of multi-class classification, automatic learning of feature and data point importance, assigning uncertainty values to outcomes, using all available data points, and does not require binary features. We show that Gradual AA-CBR performs comparably to NNs whilst significantly outperforming existing AA-CBR formulations.
Date Issued
2025-09-08
Date Acceptance
2025-04-20
Citation
Proceedings of Machine Learning Research, 2025, 284, pp.1090-1106
Publisher
MLResearchPress
Start Page
1090
End Page
1106
Journal / Book Title
Proceedings of Machine Learning Research
Volume
284
Copyright Statement
© The authors and PMLR 2025. MLResearchPress
Source
19th International Conference on Neurosymbolic Learning and Reasoning
Publication Status
Published
Start Date
2025-09-08
Finish Date
2025-09-10
Coverage Spatial
Santa Cruz, California