论文标题
机器推理解释性
Machine Reasoning Explainability
论文作者
论文摘要
作为AI领域，机器推理（MR）在很大程度上使用符号手段来形式化和模仿抽象推理。早期MR的研究显着开始询问可解释的AI（XAI），这可以说是当今AI社区的最大问题之一。从那以后，就可以解释的MR以及MR的解释性方法一直在继续。它在现代MR分支机构中尤其有效，例如论证，约束和逻辑编程，计划。我们特此旨在为MR解释性技术和研究提供选择性概述，希望从这一长期研究中的见解可以很好地补充当前的XAI景观。本文档报告了我们关于MR解释性的工作中的工作。
As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and emulate abstract reasoning. Studies in early MR have notably started inquiries into Explainable AI (XAI) -- arguably one of the biggest concerns today for the AI community. Work on explainable MR as well as on MR approaches to explainability in other areas of AI has continued ever since. It is especially potent in modern MR branches, such as argumentation, constraint and logic programming, planning. We hereby aim to provide a selective overview of MR explainability techniques and studies in hopes that insights from this long track of research will complement well the current XAI landscape. This document reports our work in-progress on MR explainability.
