Toward Robust Policy Summarization

AI agents are being developed to help people with high stakes decision-making processes from driving cars to prescribing drugs. It is therefore becoming increasingly important to develop "explainable AI" methods that help people understand the behavior of such agents. Summaries of agent policies can help human users anticipate agent behavior and facilitate more effective collaboration. Prior work has framed agent summarization as a machine teaching problem where examples of agent behavior are chosen to maximize reconstruction quality under the assumption that people do inverse reinforcement learning to infer an agent's policy from demonstrations. We compare summaries generated under this assumption to summaries generated under the assumption that people use imitation learning. We show through simulations that in some domains, there exist summaries that produce high-quality reconstructions under different models, but in other domains, only matching the summary extraction model to the reconstruction model produces high-quality reconstructions. These results highlight the importance of assuming correct computational models for how humans extrapolate from a summary, suggesting human-in-the-loop approaches to summary extraction.