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Published Articles >> Table of Contents >> Abstract
Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1 (AAMAS'04)
pp. 136-143
Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs
Rosemary Emery-Montemerlo, Carnegie Mellon University
Geoff Gordon, Carnegie Mellon University
Jeff Schneider, Carnegie Mellon University
Sebastian Thrun, Stanford University
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AAMAS.2004.10017
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| Abstract |
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Partially observable decentralized decision making in
robot teams is fundamentally different from decision making
in fully observable problems. Team members cannot simply
apply single-agent solution techniques in parallel. Instead,
we must turn to game theoretic frameworks to correctly
model the problem. While partially observable stochastic
games (POSGs) provide a solution model for decentralized
robot teams, this model quickly becomes intractable.
We propose an algorithm that approximates POSGs as a
series of smaller, related Bayesian games, using heuristics
such as QMDP to provide the future discounted value of actions.
This algorithm trades off limited look-ahead in uncertainty
for computational feasibility, and results in policies
that are locally optimal with respect to the selected heuristic.
Empirical results are provided for both a simple problem
for which the full POSG can also be constructed, as
well as more complex, robot-inspired, problems.
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 Additional Information
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Citation:
Rosemary Emery-Montemerlo, Geoff Gordon, Jeff Schneider, Sebastian Thrun,
"Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs,"
aamas,
pp. 136-143,
Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1 (AAMAS'04),
2004
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