论文标题
可解释的机器学习的绑架和论证：职位调查
Abduction and Argumentation for Explainable Machine Learning: A Position Survey
论文作者
论文摘要
本文将绑架和论证作为推理的两种原则形式，并充实了他们在机器学习中可以发挥的基本作用。它回顾了过去几十年来的最新作品与机器学习工作的链接有关，并从中阐述了解释和论证的产生作用如何使它们自然地建立了可解释的机器学习和AI系统的自然拟合机制。绑架通过通过数据的转换，制备和均质化来促进学习来促进这一目标。作为经典演绎推理的保守性扩展，论证为学习提供了灵活的预测和覆盖机制 - 一种相关的学习知识的目标语言 - 明确承认在学习的背景下，具有不确定，不确定和不一致的数据，与任何经典的逻辑理论都不矛盾。
This paper presents Abduction and Argumentation as two principled forms for reasoning, and fleshes out the fundamental role that they can play within Machine Learning. It reviews the state-of-the-art work over the past few decades on the link of these two reasoning forms with machine learning work, and from this it elaborates on how the explanation-generating role of Abduction and Argumentation makes them naturally-fitting mechanisms for the development of Explainable Machine Learning and AI systems. Abduction contributes towards this goal by facilitating learning through the transformation, preparation, and homogenization of data. Argumentation, as a conservative extension of classical deductive reasoning, offers a flexible prediction and coverage mechanism for learning -- an associated target language for learned knowledge -- that explicitly acknowledges the need to deal, in the context of learning, with uncertain, incomplete and inconsistent data that are incompatible with any classically-represented logical theory.
