Ad hoc teamwork has been introduced as a general challenge for AI and especially multiagent systems. The goal is to enable autonomous agents to band together with previously unknown teammates towards a common goal: collaboration without pre-coordination. A long-term vision for ad hoc teamwork is to enable robots or other autonomous agents to exhibit the sort of flexibility and adaptability on complex tasks that people do, for example when they play games of "pick-up" basketball or soccer. As a testbed for ad hoc teamwork, autonomous robots have played in pick-up soccer games, called "drop-in player challenges", at the international RoboCup competition. An open question is how best to evaluate ad hoc teamwork performance—how well agents are able to coordinate and collaborate with unknown teammates—of agents with different skill levels and abilities competing in drop-in player challenges. This paper presents new metrics for assessing ad hoc teamwork performance, specifically attempting to isolate an agent’s coordination and teamwork from its skill level, during drop-in player challenges. Additionally, the paper considers how to account for only a relatively small number of pick-up games being played when evaluating drop-in player challenge participants.

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In Autonomous Agents and Multiagent Systems, AAMAS 2017 Workshops, Best Papers, Gita Sukthankar and Juan A. Rodriguez-Aguilar (Eds.), pp. 168--186 2017. Springer International Publishing.

@incollection{LNAI17-MacAlpine, title={Evaluating Ad Hoc Teamwork Performance in Drop-In Player Challenges}, author={Patrick MacAlpine and Peter Stone}, booktitle={Autonomous Agents and Multiagent Systems, AAMAS 2017 Workshops, Best Papers}, editor={Gita Sukthankar and Juan A. Rodriguez-Aguilar}, publisher={Springer International Publishing}, pages={168--186}, url="http://www.cs.utexas.edu/users/ai-lab?LNAI17-MacAlpine", year={2017} }

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