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.
翻译:作为大赦国际的一个领域,机器理据(MR)主要使用象征性手段正式确定和效仿抽象推理。早期的MR研究尤其开始调查可解释的AI(XAI) -- -- 可以说是当今AI界的最大关注之一。自那以后,关于可解释的MR和可解释性MR方法的工作一直在继续进行。在现代MR分支中,这特别有效,例如论证、约束和逻辑规划、规划。我们特此对MR的解释技巧和研究进行有选择的概述,希望从这一漫长的研究轨迹中获得的见解能够很好地补充目前XAI的景观。本文件报告了我们在MR解释性方面正在进行的工作。