Cassandra, A. 1999. Tony's pomdp page. http://www.cs.brown.edu/research/ ai/pomdp/index.html.
Izadi, M. T.; Rajwade, A. V.; and Precup, D. 2005. Using core beliefs for point-based value iteration. In IJCAI.
Jaeger, H. 2000. Observable operator processes and conditioned continuation representations. Neural Computation 12(6):1371-1398.
James, M. R., and Singh, S. 2005. Planning in models that combine memory with predictive representations of state. In 20th National Conference on Artificial Intelligence.
James, M. R.; Singh, S.; and Littman, M. L. 2004. Planning with predictive state representations. In The 2004 International Conference on Machine Learning and Applications.
James, M. R.; Singh, S.; and Wolfe, B. 2005. Combining memory and landmarks with predictive state representations. In The 2005 International Joint Conference on Artificial Intelligence.
Littman, M. L.; Sutton, R. S.; and Singh, S. 2001. Predictive representations of state. In Advances In Neural Information Processing Systems 14.
Pineau, J.; Gordon, G.; and Thrun, S. 2003. Point-based value iteration: An anytime algorithm for POMDPs. In Proc. 18th Int. Joint Conf. on Artificial Intelligence.
Shani, G.; Brafman, R.; and Shimony, S. 2005. Model-based online learning of POMDPs. In ECML.
Singh, S.; James, M. R.; and Rudary, M. R. 2004. Predictive state representations, a new theory for modeling dynamical systems. In 20th Conference on Uncertainty in Artificial Intelligence.
Spaan, M., and Vlassis, N. 2005. Perseus: Randomized point-based value iteration for pomdps. Journal of Artificial Intelligence Research 24:195-220.
Sutton, R. S., and Tanner, B. 2004. Temporal-difference networks. In Advances in Neural Information Processing Systems 17.
Wolfe, B.; James, M. R.; and Singh, S. 2005. Learning predictive state representations in dynamical systems without reset. In 22nd International Conference on Machine Learning.
