Teams of agents may not always be developed in a planned, coordinated fashion. Rather, as deployed agents become more common in e-commerce and other settings, there are increasing opportunities for previously unacquainted agents to cooperate in ad hoc team settings. In such scenarios, it is useful for individual agents to be able to collaborate with a wide variety of possible teammates under the philosophy that not all agents are fully rational. This paper considers an agent that is to interact repeatedly with a teammate that will adapt to this interaction in a particular suboptimal, but natural way. We formalize this setting in game-theoretic terms, provide and analyze a fully-implemented algorithm for finding optimal action sequences, prove some theoretical results pertaining to the lengths of these action sequences, and provide empirical results pertaining to the prevalence of our problem of interest in random interaction settings.

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In {AAMAS} Workshop on Agent Mediated Electronic Commerce, pp. 153-167, May 2009.

@InProceedings{AMEC09, title={Leading a Best-Response Teammate in an Ad Hoc Team}, author={Peter Stone and Gal A. Kaminka and Jeffrey S. Rosenschein}, booktitle={{AAMAS} Workshop on Agent Mediated Electronic Commerce}, month={May}, pages={153-167}, url="http://www.cs.utexas.edu/users/ai-lab?stone:amec09", year={2009} }