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. This paper adopts an approach of learning policies to cooperate with past teammates and reusing these policies to quickly adapt to the new teammates. This approach is applied to the complex domain of robot soccer in the form of half field offense in the RoboCup simulated 2D league. This paper represents a preliminary investigation into this domain and presents a promising approach for tackling this problem.

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In AAAI Workshop on Multiagent Interaction without Prior Coordination (MIPC 2014), July 2014.

@inproceedings{MIPC14-Barrett, title={Cooperating with Unknown Teammates in Robot Soccer}, author={Samuel Barrett and Peter Stone}, booktitle={AAAI Workshop on Multiagent Interaction without Prior Coordination (MIPC 2014)}, month={July}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127468", year={2014} }

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