*Sven Seuken, Shlomo Zilberstein*

Decentralized decision making under uncertainty has been shown to be intractable when each agent has different partial information about the domain. Thus, improving the applicability and scalability of planning algorithms is an important challenge. We present the first memory-bounded dynamic programming algorithm for finite-horizon decentralized POMDPs. A set of heuristics is used to identify relevant points of the infinitely large belief space. Using these belief points, the algorithm successively selects the best joint policies for each horizon. The algorithm is extremely efficient, having linear time and space complexity with respect to the horizon length. Experimental results show that it can handle horizons that are multiple orders of magnitude larger than what was previously possible, while achieving the same or better solution quality. These results significantly increase the applicability of decentralized decision-making techniques.