Peter Stone's Selected Publications

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Predictive Memory for an Inaccessible Environment

Mike Bowling, Peter Stone, and Manuela Veloso. Predictive Memory for an Inaccessible Environment. In Proceedings of the IROS-96 Workshop on RoboCup, pp. 28–34, Osaka, Japan, November 1996.
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Abstract

Inaccessible and nondeterministic environments are very common in real-world problems. One of the difficulties in these environments is representing the knowledge about the unknown aspects of the state. We present a solution to this problem for the robotic soccer domain, an inaccessible and nondeterministic environment. We developed a predictive memory model that builds a probabilistic representation of the state based on past observations. By making the right assumptions, an effective model can be created that can store and update knowledge for even the inaccessible parts of the environment. Experiments were conducted to compare the effectiveness of our approach with a simpler approach, which ignored the inaccessible parts of the environment. The experiments consisted of using the memory models in a situation of a free ball, where two players are racing after the ball to be the first to pass it or kick it to one of their teammates or the goal. The results obtained demonstrate that this predictive approach does generate an effective memory model, which outperforms a non-predictive model.

BibTeX Entry

@InProceedings(IROS96b,
        author="Mike Bowling and Peter Stone and Manuela Veloso",
        title ="Predictive Memory for an Inaccessible Environment",
        booktitle ="Proceedings of the IROS-96 Workshop on {R}obo{C}up",
        pages="28--34",
        address="Osaka, Japan",
        month ="November",year="1996",
        abstract={
                  Inaccessible and nondeterministic environments are
                  very common in real-world problems.  One of the
                  difficulties in these environments is representing
                  the knowledge about the unknown aspects of the
                  state.  We present a solution to this problem for
                  the robotic soccer domain, an inaccessible and
                  nondeterministic environment.  We developed a
                  predictive memory model that builds a probabilistic
                  representation of the state based on past
                  observations.  By making the right assumptions, an
                  effective model can be created that can store and
                  update knowledge for even the inaccessible parts of
                  the environment.  Experiments were conducted to
                  compare the effectiveness of our approach with a
                  simpler approach, which ignored the inaccessible
                  parts of the environment.  The experiments consisted
                  of using the memory models in a situation of a free
                  ball, where two players are racing after the ball to
                  be the first to pass it or kick it to one of their
                  teammates or the goal.  The results obtained
                  demonstrate that this predictive approach does
                  generate an effective memory model, which
                  outperforms a non-predictive model.
        },
        wwwnote={<a href="http://www.cs.utexas.edu/~pstone/Papers/96iros/memory/final-paper.html">HTML version</a>.},
)

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