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@InProceedings{AAMAS13-chakrado,
  author = {Doran Chakraborty and Peter Stone},
  title = {Cooperating with a Markovian Ad Hoc Teammate},
  booktitle = {Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
  location = {St. Paul, Minnesota, USA},
  month = {May},
  year = {2013},
  abstract = {
              This paper focuses on learning in the presence of a
              Markovian teammate in Ad hoc teams. A Markovian
              teammate's policy is a function of a set of discrete
              feature values derived from the joint history of
              interaction, where the feature values transition in a
              Markovian fashion on each time step. We introduce a
              novel algorithm "Learning to Cooperate with a Markovian
              teammate", or LCM, that converges to optimal
              cooperation with any Markovian teammate, and achieves
              safety with any arbitrary teammate. The novel aspect of
              LCM is the manner in which it satisfies the above two
              goals via efficient exploration and exploitation. The
              main contribution of this paper is a full specification
              and a detailed analysis of LCM's theoretical properties.
  },
}