Becker, R.; Zilberstein, S.; Lesser, V.; and Goldman, C. V. 2004. Solving transition independent decentralized Markov decision processes. Journal of Artificial Intelligence Research (JAIR) 22:423-455.
Bernstein, D. S.; Givan, R.; Immerman, N.; and Zilberstein, S. 2002. The complexity of decentralized control of Markov decision processes. Math. Oper. Res. 27(4):819-840.
Emery-Montemerlo, R.; Gordon, G.; Schneider, J.; and Thrun, S. 2004. Approximate solutions for partially observable stochastic games with common payoffs. In AA-MAS '04: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 136-143.
Emery-Montemerlo, R. 2005. Game-Theoretic Control for Robot Teams. Ph.D. Dissertation, Carnegie Mellon University.
Gmytrasiewicz, P. J., and Doshi, P. 2005. A framework for sequential planning in multi-agent settings. Journal of Artificial Intelligence Research 24:49-79.
Hansen, E. A.; Bernstein, D. S.; and Zilberstein, S. 2004. Dynamic programming for partially observable stochastic games. In AAAI '04: Proceedings of the Nineteenth National Conference on Artificial Intelligence, 709-715.
Kaelbling, L. P.; Littman, M. L.; and Cassandra, A. R. 1998. Planning and acting in partially observable stochastic domains. Artif. Intell. 101(1-2):99-134.
Littman, M.; Cassandra, A.; and Kaelbling, L. 1995. Learning policies for partially observable environments: Scaling up. In International Conference on Machine Learning, 362-370.
Nair, R.; Tambe, M.; Yokoo, M.; Pynadath, D. V.; and Marsella, S. 2003. Taming decentralized POMDPs: Towards efficient policy computation for multiagent settings. In IJCAI-03, Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, 705-711.
Puterman, M. L. 1994. Markov Decision Processes - Discrete Stochastic Dynamic Programming. New York, NY: John Wiley & Sons, Inc.
Szer, D.; Charpillet, F.; and Zilberstein, S. 2005. MAA*: A heuristic search algorithm for solving decentralized POMDPs. In Proceedings of the Twenty First Conference on Uncertainty in Artificial Intelligence.