HIGHLIGHTS: Summarizing Agent Behavior to People

People increasingly interact with autonomous agents. This paper introduces and formalizes the problem of automatically generating a summary of an agent's behavior with the goal of increasing people's familiarity with the agent's capabilities and limitations. In contrast with prior approaches which developed methods for explaining a single decision made by an agent, our approach aims to provide users with a summary that describes the agent's behavior in different situations. We hypothesize that reviewing such summaries could help people in tasks such as choosing between agents or determining the level of autonomy to grant to an agent. We develop "HIGHLIGHTS'', an algorithm that produces a summary of an agent's behavior by extracting important trajectories from simulations of the agent. We conducted a human-subject experiment to evaluate whether HIGHLIGHTS summaries help people assess the capabilities of agents. Our results show that participants were more successful at evaluating the capabilities of agents when presented with HIGHLIGHTS summaries compared to baseline summaries, and rated them as more helpful. We also explore a variant of the HIGHLIGHTS algorithm which aims to increase the diversity of states included in the summary, and show that this modification further improves people's ability to assess agents' capabilities.