@conference {1506775,
	title = {DCOPs Meet the RealWorld: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks },
	booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
	year = {2009},
	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.},
	author = {Jain, Manish and Matthew Taylor and Tambe, Milind and Makoto Yokoo}
}
