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@incollection{LNAI17-MacAlpine,
  author = {Patrick MacAlpine and Peter Stone},
  title = {Evaluating Ad Hoc Teamwork Performance in Drop-In Player Challenges},
  booktitle = {Autonomous Agents and Multiagent Systems, AAMAS 2017 Workshops, Best Papers},
  Editor={Gita Sukthankar and Juan A. Rodriguez-Aguilar},
  Publisher={Springer International Publishing},
  year={2017},
  pages={168--86},
  volume = {10642},
  series={Lecture Notes in Artificial Intelligence},
  abstract={
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.
  },
}