Peter Stone's Selected Publications

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To Teach or not to Teach? Decision Making Under Uncertainty in Ad Hoc Teams

To Teach or not to Teach? Decision Making Under Uncertainty in Ad Hoc Teams.
Peter Stone and Sarit Kraus.
In The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), International Foundation for Autonomous Agents and Multiagent Systems, May 2010.
supplemental material cited in the paper, including a proof and an algorithm.
AAMAS 2010

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Abstract

In typical multiagent teamwork settings, the teammates are either programmed together, or are otherwise provided with standard communication languages and coordination protocols. In contrast, this paper presents an ad hoc team setting in which the teammates are not pre-coordinated, yet still must work together in order to achieve their common goal(s). We represent a specific instance of this scenario, in which a teammate has limited action capabilities and a fixed and known behavior, as a finite-horizon, cooperative $k$-armed bandit. In addition to motivating and studying this novel ad hoc teamwork scenario, the paper contributes to the $k$-armed bandits literature by characterizing the conditions under which certain actions are potentially optimal, and by presenting a polynomial dynamic programming algorithm that solves for the optimal action when the arm payoffs come from a discrete distribution.

BibTeX Entry

@InProceedings{AAMAS10-adhoc,
  author="Peter Stone and Sarit Kraus",
  title = {To Teach or not to Teach? Decision Making Under Uncertainty in Ad Hoc Teams},
  booktitle = "The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS)",
  location = "Toronto, Canada",
  month = "May",
  year = "2010",
  publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
  abstract  = {
               In typical multiagent \emph{teamwork} settings, the
               teammates are either programmed together, or are
               otherwise provided with standard communication
               languages and coordination protocols.  In contrast,
               this paper presents an \emph{ad hoc team} setting in
               which the teammates are not pre-coordinated, yet still
               must work together in order to achieve their common
               goal(s).  We represent a specific instance of this
               scenario, in which a teammate has limited action
               capabilities and a fixed and known behavior, as a
               finite-horizon, cooperative $k$-armed bandit.  In
               addition to motivating and studying this novel ad hoc
               teamwork scenario, the paper contributes to the
               $k$-armed bandits literature by characterizing the
               conditions under which certain actions are potentially
               optimal, and by presenting a polynomial dynamic
               programming algorithm that solves for the optimal
               action when the arm payoffs come from a discrete
               distribution.
  },
  wwwnote={<a href="http://www.cs.utexas.edu/~pstone/Papers/2010aamas/supplemental.pdf">supplemental material</a> cited in the paper, including a proof and an algorithm.<br> <a href="http://www.cse.yorku.ca/AAMAS2010/">AAMAS 2010</a>},
}

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