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Teamwork with Limited Knowledge of Teammates.
Samuel Barrett, Peter Stone, Sarit Kraus, and Avi Rosenfeld.
In Proceedings of the Twenty-Seventh AAAI Conference on
Artificial Intelligence, July 2013.
[PDF]190.1kB [postscript]2.0MB
While great strides have been made in multiagent teamwork, existing approaches typically assume extensive information exists about teammates and how to coordinate actions. This paper addresses how robust teamwork can still be created even if limited or no information exists about a specific group of teammates, as in the ad hoc teamwork scenario. The main contribution of this paper is the first empirical evaluation of an agent cooperating with teammates not created by the authors, where the agent is not provided expert knowledge of its teammates. For this purpose, we develop a general-purpose teammate modeling method and test the resulting ad hoc team agent's ability to collaborate with more than 40 unknown teams of agents to accomplish a benchmark task. These agents were designed by people other than the authors without these designers planning for the ad hoc teamwork setting. A secondary contribution of the paper is a new transfer learning algorithm, TwoStageTransfer, that can improve results when the ad hoc team agent does have some limited observations of its current teammates.
@InProceedings{AAAI13-Barrett,
author = {Samuel Barrett and Peter Stone and Sarit Kraus and Avi Rosenfeld},
title = {Teamwork with Limited Knowledge of Teammates},
booktitle = {Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence},
location = {Bellevue, Washington, USA},
month = {July},
year = {2013},
abstract={
While great strides have been made in multiagent teamwork, existing approaches typically assume extensive information exists about teammates and how to coordinate actions. This paper addresses how robust teamwork can still be created even if limited or no information exists about a specific group of teammates, as in the ad hoc teamwork scenario. The main contribution of this paper is the first empirical evaluation of an agent cooperating with teammates not created by the authors, where the agent is not provided expert knowledge of its teammates. For this purpose, we develop a general-purpose teammate modeling method and test the resulting ad hoc team agent's ability to collaborate with more than 40 unknown teams of agents to accomplish a benchmark task. These agents were designed by people other than the authors without these designers planning for the ad hoc teamwork setting. A secondary contribution of the paper is a new transfer learning algorithm, TwoStageTransfer, that can improve results when the ad hoc team agent does have some limited observations of its current teammates.
}
}
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