A desirable goal for autonomous agents is to be able to coordinate on the fly with previously unknown teammates. Known as “ad hoc teamwork”, enabling such a capability has been receiving increasing attention in the research community. One of the central challenges in ad hoc teamwork is quickly recognizing the current plans of other agents and planning accordingly. In this paper, we focus on the scenario in which teammates can communicate with one another, but only at a cost. Thus, they must carefully balance plan recognition based on observations vs. that based on communication. This paper proposes a new metric for evaluating how similar are two policies that a teammate may be following - the Expected Divergence Point (EDP). We then present a novel planning algorithm for ad hoc teamwork, determining which query to ask and planning accordingly. We demonstrate the effectiveness of this algorithm in a range of increasingly general communication in ad hoc teamwork problems.

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In Proceedings of the 35th Conference on Artificial Intelligence (AAAI), February 2021.

@inproceedings{AAAI21-Macke, title={Expected Value of Communication for Planning in Ad Hoc Teamwork}, author={William Macke and Reuth Mirsky and Peter Stone}, booktitle={Proceedings of the 35th Conference on Artificial Intelligence (AAAI)}, month={February}, url="http://www.cs.utexas.edu/users/ai-lab?AAAI21-Macke", year={2021} }