2024
PPS: Personalized Policy Summarization for Explaining Sequential Behavior of Autonomous Agents
Harrison Huang*, Peizhu Qian*, Vaibhav Unhelkar (* equal contribution)
AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2024 Spotlight
Policy summarization methods aim to showcase 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.
PPS: Personalized Policy Summarization for Explaining Sequential Behavior of Autonomous Agents
Harrison Huang*, Peizhu Qian*, Vaibhav Unhelkar (* equal contribution)
AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2024 Spotlight
Policy summarization methods aim to showcase 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.