Matthew E. Taylor's Publications

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DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks

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 Third International Workshop on Agent Technology for Sensor Networks (at AAMAS-09), May 2009.
ATSN-2009
Superseded by the IJCAI-09 conference paper DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks.

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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.

BibTeX Entry

@inproceedings(ATSN09-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 Third International Workshop on Agent Technology for Sensor Networks (at AAMAS-09)",
  month="May",
  year= "2009",
  wwwnote={<a
  href="http://www.atsn09.org">ATSN-2009</a><br>Superseded by the
  IJCAI-09 conference paper <a
  href="http://teamcore.usc.edu/taylorm/Publications/b2hd-IJCAI09-Jain.html">DCOPs
  Meet the Real World: Exploring Unknown Reward Matrices with
  Applications to Mobile Sensor Networks</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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