Adoption and deployment of robotic and autonomous systems in industry are currently hindered by the lack of transparency, required for safety and accountability. Methods for providing explanations are needed that are agnostic to the underlying autonomous system and easily updated. Furthermore, different stakeholders with varying levels of expertise, will require different levels of information. In this work, we use surrogate models to provide transparency as to the underlying policies for behaviour activation. We show that these surrogate models can effectively break down autonomous agents' behaviour into explainable components for use in natural language explanations.

翻译：工业领域机器人与自主系统的采用与部署目前因缺乏安全性和责任追究所需的透明度而受阻。需要提供解释的方法，这些方法应独立于底层自主系统且易于更新。此外，不同专业水平的相关方需要不同层次的信息。本研究利用代理模型揭示行为激活的底层策略的透明度。我们证明这些代理模型能够有效将自主智能体的行为分解为可解释的组件，用于生成自然语言解释。