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Samuel Barrett and Peter Stone. Cooperating with Unknown Teammates in Complex Domains: A Robot Soccer Case Study of Ad Hoc Teamwork. In Proceedings of the Twenty-Ningth AAAI Conference on Artificial Intelligence, January 2015.
Many scenarios require that robots work together as a team in order to effectively accomplish their tasks. However, pre-coordinating these teams may not always be possible given the growing number of companies and research labs creating these robots. Therefore, it is desirable for robots to be able to reason about ad hoc teamwork and adapt to new teammates on the fly. Past research on ad hoc teamwork has focused on relatively simple domains, but this paper demonstrates that agents can reason about ad hoc teamwork in complex scenarios. To handle these complex scenarios, we introduce a new algorithm, PLASTIC-Policy, that builds on an existing ad hoc teamwork approach. Specifically, PLASTIC-Policy learns policies to cooperate with past teammates and reuses these policies to quickly adapt to new teammates. This approach is tested in the 2D simulation soccer league of RoboCup using the half field offense task.
@InProceedings{AAAI15-Barrett,
author = {Samuel Barrett and Peter Stone},
title = {Cooperating with Unknown Teammates in Complex Domains: A Robot Soccer Case Study of Ad Hoc Teamwork},
booktitle = {Proceedings of the Twenty-Ningth AAAI Conference on Artificial Intelligence},
location = {Austin, Texas, USA},
month = {January},
year = {2015},
abstract={
Many scenarios require that robots work together as a team in order to
effectively accomplish their tasks. However, pre-coordinating these teams
may not always be possible given the growing number of companies and research
labs creating these robots. Therefore, it is desirable for robots to be able
to reason about ad hoc teamwork and adapt to new teammates on the fly. Past
research on ad hoc teamwork has focused on relatively simple domains, but
this paper demonstrates that agents can reason about ad hoc teamwork in
complex scenarios. To handle these complex scenarios, we introduce a new
algorithm, PLASTIC-Policy, that builds on an existing ad hoc teamwork
approach. Specifically, PLASTIC-Policy learns policies to cooperate with
past teammates and reuses these policies to quickly adapt to new teammates.
This approach is tested in the 2D simulation soccer league of RoboCup using
the half field offense task.
}
}
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