论文标题
“黑匣子”分类器系统的逻辑
A Logic of "Black Box" Classifier Systems
论文作者
论文摘要
传统上，二进制分类器是通过命题逻辑（PL）研究的。在假设下面的布尔函数是完全已知的假设下，PL只能将它们表示为白框。但是，在实用应用和通过机器学习培训的二进制分类器是不透明的。它们通常被描述为黑匣子。在本文中，我们提供了一种称为PLC（二进制输入分类器的产品模态逻辑）的产品模式逻辑，其中“黑匣子”的概念被解释为一组分类器的不确定性。我们给出了有关逻辑的公理学和满意度检查的复杂性的结果。此外，我们提出了一个动态扩展，其中可以表示有关实际分类器的新信息的过程。
Binary classifiers are traditionally studied by propositional logic (PL). PL can only represent them as white boxes, under the assumption that the underlying Boolean function is fully known. Binary classifiers used in practical applications and trained by machine learning are however opaque. They are usually described as black boxes. In this paper, we provide a product modal logic called PLC (Product modal Logic for binary input Classifier) in which the notion of "black box" is interpreted as the uncertainty over a set of classifiers. We give results about axiomatics and complexity of satisfiability checking for our logic. Moreover, we present a dynamic extension in which the process of acquiring new information about the actual classifier can be represented.
