Peter Stone's Selected Publications

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Towards Employing PSRs in a Continuous Domain

Nicholas K. Jong and Peter Stone. Towards Employing PSRs in a Continuous Domain. Technical Report UT-AI-TR-04-309, The University of Texas at Austin, Department of Computer Sciences, AI Laboratory, 2004.
UTAustin AI Lab technical reports

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Abstract

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.

BibTeX Entry

@TechReport(psr-note04,
        Author="Nicholas K.\ Jong and Peter Stone",
        title="Towards Employing {PSR}s in a Continuous Domain",
        Institution="The University of Texas at Austin, Department of Computer Sciences, AI Laboratory",
        number="UT-AI-TR-04-309",
        year="2004",month="February",
        abstract={
                  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.
        },
        wwwnote={<a href="http://www.cs.utexas.edu/research/publications/">UT
Austin AI Lab technical reports</a>},
)

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