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Manish Jain, Matthew E. Taylor,
Makoto Yokoo, and Milind Tambe.
DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks. In Proceedings
of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI), July 2009.
IJCAI-2009
[PDF]250.8kB [postscript]1.9MB
Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to real-world domains. Three fundamental challenges must be addressed for a class of real-world domains, requiring novel DCOP algorithms. First, agents may not know the payoff matrix and must explore the environment to determine rewards associated with variable settings. Second, agents may need to maximize total accumulated reward rather than instantaneous final reward. Third, limited time horizons disallow exhaustive exploration of the environment. We propose and implement a set of novel algorithms that combine decision-theoretic exploration approaches with DCOP-mandated coordination. In addition to simulation results, we implement these algorithms on robots, deploying DCOPs on a distributed mobile sensor network.
@inproceedings(IJCAI09-Jain,
author="Manish Jain and Matthew E.\ Taylor and Makoto Yokoo and Milind Tambe",
title="DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks",
Booktitle="Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence ({IJCAI})",
month="July",
year= "2009",
wwwnote={<a href="http://www.ijcai-09.org">IJCAI-2009</a>},
abstract={Buoyed by recent successes in the area of distributed
constraint optimization problems (DCOPs), this paper addresses
challenges faced when applying DCOPs to real-world domains. Three
fundamental challenges must be addressed for a class of real-world
domains, requiring novel DCOP algorithms. First, agents may not
know the payoff matrix and must explore the environment to
determine rewards associated with variable settings. Second,
agents may need to maximize total accumulated reward rather than
instantaneous final reward. Third, limited time horizons disallow
exhaustive exploration of the environment. We propose and
implement a set of novel algorithms that combine
decision-theoretic exploration approaches with DCOP-mandated
coordination. In addition to simulation results, we implement
these algorithms on robots, deploying DCOPs on a distributed
mobile sensor network.},
)
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