Predictive state representation

In computer science, a predictive state representation (PSR) is a way to model a state of controlled dynamical system from a history of actions taken and resulting observations. PSR captures the state of a system as a vector of predictions for future tests (experiments) that can be done on the system.[1] A test is a sequence of action-observation pairs and its prediction is the probability of the test's observation-sequence happening if the test's action-sequence were to be executed on the system. One of the advantage of using PSR is that the predictions are directly related to 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.[2]

References

  1. ↑ James, Michael R.; Singh, Satinder (2004-01-01). "Learning and Discovery of Predictive State Representations in Dynamical Systems with Reset". Proceedings of the Twenty-first International Conference on Machine Learning. ICML '04. New York, NY, USA: ACM: 53–. doi:10.1145/1015330.1015359. ISBN 1581138385.
  2. ↑ "A Planning Algorithm for Predictive State Representations (PDF) - Semantic Scholar". www.semanticscholar.org. Retrieved 2016-07-14.
  • 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)


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