PPS: Personalized Policy Summarization for Explaining Sequential Behavior of Autonomous Agents

Abstract

AI-enabled agents designed to assist humans are gaining traction in a variety of domains such as healthcare and disaster response. It is evident that, as we move forward, these agents will play increasingly vital roles in our lives. To realize this future successfully and mitigate its unintended consequences, it is imperative that humans have a clear understanding of the agents that they work with. Policy summarization methods help facilitate this understanding by showcasing key examples of agent behaviors to their human users. Yet, existing methods produce “one-size-fits-all” summaries for a generic audience ahead of time. Drawing inspiration from research in pedagogy, we posit that personalized policy summaries can more effectively enhance user understanding. To evaluate this hypothesis, this paper presents and benchmarks a novel technique: Personalized Policy Summarization (PPS). PPS discerns a user’s mental model of the agent through a series of algorithmically generated questions and crafts customized policy summaries to enhance user understanding. Unlike existing methods, PPS actively engages with users to gauge their comprehension of the agent behavior, subsequently generating tailored explanations on the fly. Through a combination of numerical and human subject experiments, we confirm the utility of this personalized approach to explainable AI.