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@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={supplemental material cited in the paper, including a proof and an algorithm.
AAMAS 2010},
}