DCOPs meet the realworld: exploring unknown reward matrices with applications to mobile sensor networks

  • Authors:

  • Manish Jain;Matthew Taylor;Milind Tambe;Makoto Yokoo

  • Affiliations:

  • University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA;Kyushu University, Fukuoka, Japan

  • Venue:
  • IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
  • Year:
  • 2009

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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.