@conference {1510224,
	title = {Taming Decentralized POMDPs: Towards efficient policy computation for multiagent settings },
	booktitle = {International Joint conference on Artificial Intelligence (IJCAI)},
	year = {2003},
	abstract = {
The problem of deriving joint policies for a group
of agents that maximize some joint reward function can be modeled as a decentralized partially
observable Markov decision process (POMDP).
Yet, despite the growing importance and applications of decentralized POMDP models in the multiagents arena, few algorithms have been developed for efficiently deriving joint policies for these
models. This paper presents a new class of locally optimal algorithms called {\textquotedblleft}Joint Equilibriumbased search for policies (JESP){\textquotedblright}. We first describe
an exhaustive version of JESP and subsequently
a novel dynamic programming approach to JESP.
Our complexity analysis reveals the potential for
exponential speedups due to the dynamic programming approach. These theoretical results are verified via empirical comparisons of the two JESP
versions with each other and with a globally optimal brute-force search algorithm. Finally, we prove
piece-wise linear and convexity (PWLC) properties, thus taking steps towards developing algorithms for continuous belief states},
	author = {Ranjit Nair and Tambe, Milind and Makoto Yokoo and D. V. Pynadath and S. Marsella}
}
