论文标题
通过分类器建模和解释基于案例的推理者
Modelling and Explaining Legal Case-based Reasoners through Classifiers
论文作者
论文摘要
本文汇集了两条研究线：基于因素的基于病例的推理（CBR）的模型和分类器的逻辑规范。分类器的逻辑方法捕获分类器系统中功能和结果之间的连接。基于因素的推理是AI＆Law的先例来推理的一种流行推理方法。 Horty（2011）已将基于因子的先例模型开发为先例约束理论。在本文中，我们将模态逻辑方法（二进制输入分类器，BLC）与分类器及其解释相结合，由Liu＆Lorini（2021）和Horty对基于因子的CBR的说明，因为分类器和CBR映射集合的决策或分类。我们用BCL的语言重新重新制定了霍蒂的案例基础，并给出了几个代表性结果。此外，我们展示了CBR的概念，例如原因，理由之间的偏好可以通过分类器系统的概念来分析。
This paper brings together two lines of research: factor-based models of case-based reasoning (CBR) and the logical specification of classifiers. Logical approaches to classifiers capture the connection between features and outcomes in classifier systems. Factor-based reasoning is a popular approach to reasoning by precedent in AI & Law. Horty (2011) has developed the factor-based models of precedent into a theory of precedential constraint. In this paper we combine the modal logic approach (binary-input classifier, BLC) to classifiers and their explanations given by Liu & Lorini (2021) with Horty's account of factor-based CBR, since both a classifier and CBR map sets of features to decisions or classifications. We reformulate case bases of Horty in the language of BCL, and give several representation results. Furthermore, we show how notions of CBR, e.g. reason, preference between reasons, can be analyzed by notions of classifier system.
