Predictive State Representations (PSRs) recently emerged as an alternative framework for reasoning about stochastic environments. However, unlike Markov decision processes, they have not yet been extended to large domains or domains with continuous state variables. This report briefly describes an attempt to scale PSRs to such domains. Our goal was to construct a PSR allowing an agent to track its location on the simulated soccer field used in Robocup. This line of work ended in a negative result.

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Technical Report UT-AI-TR-04-309, The University of Texas at Austin, Department of Computer Sciences, AI Laboratory.

@TechReport{psr-note04, title={Towards Employing PSRs in a Continuous Domain}, author={Nicholas K. Jong and Peter Stone}, number={UT-AI-TR-04-309}, month={February}, institution={The University of Texas at Austin, Department of Computer Sciences, AI Laboratory}, url="http://www.cs.utexas.edu/users/ai-lab?psr-note04", year={2004} }