基于抽象论证的案例推理中案例相关性学习的技术报告
Technical Report on the Learning of Case Relevance in Case-Based Reasoning with Abstract Argumentation
AI总结：
本文研究在法律案例推理中结合抽象论证与决策树自动学习案例相关性，实验表明该方法在两个法律数据集上具有竞争力，并能生成更紧凑、更易解释的表示。
摘要：
案例推理在多个法律场景中发挥着重要作用。本文聚焦于一种近期提出的案例推理方法，该方法由抽象论证的一种实例化形式所支持，其中论证表示案例，论证之间的攻击源于案例之间的结果分歧以及一种相关性概念。在此语境下，相关性与案例之间的一种具体性形式相关联。我们探索了如何借助决策树在实践中自动学习相关性，并研究了案例推理与抽象论证（AA-CBR）的结合以及案例相关性学习在法律场景预测中的应用。具体而言，我们表明，在两个法律数据集上，AA-CBR与基于决策树的案例相关性学习相比决策树具有竞争力。我们还表明，采用基于决策树的案例相关性学习的AA-CBR能够获得比其决策树对应方法更紧凑的表示，这可能有利于获得认知上可处理的解释。
英文摘要：
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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