@conference {1506501,
	title = {Teamwork and Coordination under Model Uncertainty in DEC-POMDPs },
	booktitle = {AAAI 2010 Workshop on Interactive Decision Theory and Game Theory (IDTGT)	},
	year = {2010},
	abstract = {Distributed Partially Observable Markov Decision Processes
(DEC-POMDPs) are a popular planning framework for multiagent teamwork to compute (near-)optimal plans. However,
these methods assume a complete and correct world model,
which is often violated in real-world domains. We provide
a new algorithm for DEC-POMDPs that is more robust to
model uncertainty, with a focus on domains with sparse agent
interactions. Our STC algorithm relies on the following key
ideas: (1) reduce planning-time computation by shifting some
of the burden to execution-time reasoning, (2) exploit sparse
interactions between agents, and (3) maintain an approximate
model of agents{\textquoteright} beliefs. We empirically show that STC is
often substantially faster to existing DEC-POMDP methods
without sacrificing reward performance.},
	author = {Kwak, Jun-young and Yang, Rong and Yin, Zhengyu and Matthew E. Taylor and Tambe, Milind}
}
