@conference {1509412,
	title = {Winning back the CUP for distributed POMDPs: Planning over continuous belief spaces },
	booktitle = {Fifth International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS)},
	year = {2006},
	abstract = {Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are evolving as a popular approach for modeling
multiagent systems, and many different algorithms have been proposed to obtain locally or globally optimal policies. Unfortunately,
most of these algorithms have either been explicitly designed or
experimentally evaluated assuming knowledge of a starting belief
point, an assumption that often does not hold in complex, uncertain domains. Instead, in such domains, it is important for agents
to explicitly plan over continuous belief spaces. This paper provides a novel algorithm to explicitly compute finite horizon policies
over continuous belief spaces, without restricting the space of policies. By marrying an efficient single-agent POMDP solver with a
heuristic distributed POMDP policy-generation algorithm, locally
optimal joint policies are obtained, each of which dominates within
a different part of the belief region. We provide heuristics that significantly improve the efficiency of the resulting algorithm and provide detailed experimental results. To the best of our knowledge,
these are the first run-time results for analytically generating policies over continuous belief spaces in distributed POMDPs.},
	author = {Varakantham, Pradeep and Ranjit Nair and Tambe, Milind and Makoto Yokoo}
}
