Supported Abstract Argumentation for Case-Based Reasoning
This is an incremental improvement for AI systems using argumentation-based reasoning, addressing a specific limitation in case-based classification.
The paper tackles the problem of extraneous cases in abstract argumentation for case-based reasoning by introducing sAA-CBR, a binary classification model that uses supports to eliminate spikes while preserving key properties.
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