Machine Reasoning Explainability
It addresses the need for explainability in AI, particularly for the AI community, by reviewing existing work in Machine Reasoning, but it is incremental as it offers an overview rather than new findings.
The paper provides a selective overview of Machine Reasoning explainability techniques and studies, aiming to complement the current Explainable AI landscape by drawing insights from long-standing research in symbolic reasoning approaches.
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.