论文标题
在基于案例的推理中，谨慎的单调性与抽象论证
Cautious Monotonicity in Case-Based Reasoning with Abstract Argumentation
论文作者
论文摘要
最近，已经提出了基于案例的推理的基于抽象论证的模型（$ aa {\ text - } cbr $简称），最初是受法律领域的启发，但也适用于不同场景中的分类器，包括图像分类，文本分析，以及预测英国议会账单的通过。但是，作为推理系统的$ aa {\ text - }的形式属性仍然在很大程度上没有探索。在本文中，我们专注于分析$ aa {\ text - } cbr $的常规版本的非单调性属性（我们称为$ aa {\ text - } cbr _ {\ succeq} $）。具体来说，我们证明$ aa {\ text - } cbr _ {\ succeq} $不是谨慎的单调，这是非单调推理文献中经常被认为是可取的属性。然后，我们定义了$ aa {\ text - } cbr _ {\ succeq} $的变体，该变体谨慎单调，并提供了用于获取它的算法。此外，我们证明了这种变化等同于使用$ aa {\ text - } cbr _ {\ succeq} $，其中包含一个由原始案例库中的所有“令人惊讶”的案例组成的限制案例。
Recently, abstract argumentation-based models of case-based reasoning ($AA{\text -}CBR$ in short) have been proposed, originally inspired by the legal domain, but also applicable as classifiers in different scenarios, including image classification, sentiment analysis of text, and in predicting the passage of bills in the UK Parliament. However, the formal properties of $AA{\text -}CBR$ as a reasoning system remain largely unexplored. In this paper, we focus on analysing the non-monotonicity properties of a regular version of $AA{\text -}CBR$ (that we call $AA{\text -}CBR_{\succeq}$). Specifically, we prove that $AA{\text -}CBR_{\succeq}$ is not cautiously monotonic, a property frequently considered desirable in the literature of non-monotonic reasoning. We then define a variation of $AA{\text -}CBR_{\succeq}$ which is cautiously monotonic, and provide an algorithm for obtaining it. Further, we prove that such variation is equivalent to using $AA{\text -}CBR_{\succeq}$ with a restricted casebase consisting of all "surprising" cases in the original casebase.
