Technical Report on the Learning of Case Relevance in Case-Based Reasoning with Abstract Argumentation

• URL: http://arxiv.org/abs/2310.19607v1
• Date: Mon, 30 Oct 2023 15:01:41 GMT
• Title: Technical Report on the Learning of Case Relevance in Case-Based Reasoning with Abstract Argumentation
• Authors: Guilherme Paulino-Passos, Francesca Toni
• Abstract summary: We show how relevance can be learnt automatically in practice with the help of decision trees. We also show that AA-CBR with decision-tree-based learning of case relevance results in a more compact representation than their decision tree counterparts.
• Score: 14.755026411356315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Case-based reasoning is known to play an important role in several legal settings. In this paper we focus on a recent approach to case-based reasoning, supported by an instantiation of abstract argumentation whereby arguments represent cases and attack between arguments results from outcome disagreement between cases and a notion of relevance. In this context, relevance is connected to a form of specificity among cases. We explore how relevance can be learnt automatically in practice with the help of decision trees, and explore the combination of case-based reasoning with abstract argumentation (AA-CBR) and learning of case relevance for prediction in legal settings. Specifically, we show that, for two legal datasets, AA-CBR and decision-tree-based learning of case relevance perform competitively in comparison with decision trees. We also show that AA-CBR with decision-tree-based learning of case relevance results in a more compact representation than their decision tree counterparts, which could be beneficial for obtaining cognitively tractable explanations.

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