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@InProceedings{MIPC17-MacAlpine,
author = {Patrick MacAlpine and Peter Stone},
title = {Evaluating Ad Hoc Teamwork Performance in Drop-In Player Challenges},
booktitle = {AAMAS Multiagent Interaction without Prior Coordination (MIPC) Workshop},
location = {S\~ao Paulo, Brazil},
month = {May},
year = {2017},
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.
},
}