Predictive state representation

In computer science, a predictive state representation (PSR) is a dynamical system representation that keeps track of the state of the system using predictions of future observations. A PSR's state is grounded directly to statistics over observable quantities. This is in contrast to other models of dynamical systems, such as partially observable Markov decision processes (POMDPs) where the state of the system is represented as a probability distribution over unobserved nominal states.

References

• Littman, Michael L.; Richard S. Sutton; Satinder Singh (2002). "Predictive Representations of State" (PDF). Advances in Neural Information Processing Systems 14 (NIPS). pp. 1555–1561.

• Singh, Satinder; Michael R. James; Matthew R. Rudary (2004). "Predictive State Representations: A New Theory for Modeling Dynamical Systems" (PDF). Uncertainty in Artificial Intelligence: Proceedings of the Twentieth Conference (UAI). pp. 512–519.

• Wiewiora, Eric Walter (2008), Modeling Probability Distributions with Predictive State Representations (PDF)