Reviews: On the Utility of Learning about Humans for Human-AI Coordination

The paper proposes a new evaluation framework and benchmark for multi-agent learning settings where coordination with team mates is required to complete a task, and carefully evaluates state-of-the-art learning approaches in this novel setting, including evaluation with human players. All reviewers agreed that the contributions made by the paper are high, and are likely to influence future work in this field. In the initial reviews, several areas of improvement were noted, including to precisely explain the relationship of this work to the substantial amount of prior work in human-robot and human-AI interaction, several requests for clarification, and suggestions for further experimentation. The reviewers were content with the author response, and in particular the provided clarification of the relationship to prior work and overall contribution of the paper. I encourage the authors to carefully consider all reviewer comments when preparing the camera ready version.