Supported Abstract Argumentation for Case-Based Reasoning
Artificial Intelligence
2025-07-08 v1
Abstract
We introduce Supported Abstract Argumentation for Case-Based Reasoning (sAA-CBR), a binary classification model in which past cases engage in debates by arguing in favour of their labelling and attacking or supporting those with opposing or agreeing labels. With supports, sAA-CBR overcomes the limitation of its precursor AA-CBR, which can contain extraneous cases (or spikes) that are not included in the debates. We prove that sAA-CBR contains no spikes, without trading off key model properties
Keywords
Cite
@article{arxiv.2507.04994,
title = {Supported Abstract Argumentation for Case-Based Reasoning},
author = {Adam Gould and Gabriel de Olim Gaul and Francesca Toni},
journal= {arXiv preprint arXiv:2507.04994},
year = {2025}
}
Comments
Accepted to IARML@ICJAI2025: Workshop on the Interactions between Analogical Reasoning and Machine Learning