@conference {1510008,
	title = {Communication for Improving Policy Computation in Distributed POMDPs },
	booktitle = {Third International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-04)},
	year = {2004},
	abstract = {Distributed Partially Observable Markov Decision
Problems (POMDPs) are emerging as a popular approach for modeling multiagent teamwork where a group
of agents work together to jointly maximize a reward
function. Since the problem of finding the optimal joint
policy for a distributed POMDP has been shown to be
NEXP-Complete if no assumptions are made about the
domain conditions, several locally optimal approaches
have emerged as a viable solution. However, the use of
communicative actions as part of these locally optimal algorithms has been largely ignored or has been applied only
under restrictive assumptions about the domain. In this
paper, we show how communicative acts can be explicitly introduced in order to find locally optimal joint policies that allow agents to coordinate better through synchronization achieved via communication. Furthermore,
the introduction of communication allows us to develop a
novel compact policy representation that results in savings of both space and time which are verified empirically. Finally, through the imposition of constraints on
communication such as not going without communicating for more than K steps, even greater space and time
savings can be obtained.},
	author = {Ranjit Nair and Makoto Yokoo and Maayan Roth and Tambe, Milind}
}
