@InProceedings{jong05a,
  author =	 {Nicholas K. Jong and Peter Stone},
  title =	 {State Abstraction Discovery from Irrelevant State
                  Variables},
  booktitle =	 {Proceedings of the Nineteenth International Joint
                  Conference on Artificial Intelligence},
  pages =	 {752--757},
  year =	 2005,
  abstract =	 {Abstraction is a powerful form of domain knowledge
                  that allows reinforcement-learning agents to cope
                  with complex environments, but in most cases a human
                  must supply this knowledge. In the absence of such
                  prior knowledge or a given model, we propose an
                  algorithm for the automatic discovery of state
                  abstraction from policies learned in one domain for
                  use in other domains that have similar structure. To
                  this end, we introduce a novel condition for state
                  abstraction in terms of the relevance of state
                  features to optimal behavior, and we exhibit
                  statistical methods that detect this condition
                  robustly. Finally, we show how to apply temporal
                  abstraction to benefit safely from even partial
                  state abstraction in the presence of generalization
                  error.},
  cluster = 	 {8680786470331608686},
  url = 	 {http://jmvidal.cse.sc.edu/library/jong05a.pdf}
}