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.

在这篇论文中，我们关注于一种最近的基于案例的推理方法，通过采用抽象论证的实例来支持案例推理，其中论证代表案例，论证之间的攻击来自于案例之间的结果分歧与关联概念。在这个背景下，相关性与案例的特异性形式有关。我们探讨如何通过决策树的帮助在实践中自动学习相关性，并研究在法律环境中案例推理与抽象论证（AA-CBR）以及案例相关性学习在预测方面的组合。具体而言，我们表明，在两个法律数据集中，AA-CBR 和基于决策树的案例相关性学习与决策树相比具有竞争力。我们还表明，AA-CBR 结合基于决策树的案例相关性学习具有比决策树更紧凑的表示形式，这对于获得认知可处理的解释可能是有益的。