Machine Reasoning Explainability

• URL: http://arxiv.org/abs/2009.00418v2
• Date: Tue, 1 Dec 2020 17:09:03 GMT
• Title: Machine Reasoning Explainability
• Authors: Kristijonas Cyras, Ramamurthy Badrinath, Swarup Kumar Mohalik, Anusha Mujumdar, Alexandros Nikou, Alessandro Previti, Vaishnavi Sundararajan, Aneta Vulgarakis Feljan
• Abstract summary: 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) This document reports our work in-progress on MR explainability.
• Score: 100.78417922186048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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