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    Playing the Matching-Shoulders Lob-Pass Game with Logarithmic Regret


    Kilian, Joe and Lang, Kevin J. and Pearlmutter, Barak A. (1994) Playing the Matching-Shoulders Lob-Pass Game with Logarithmic Regret. In: Proceedings of the seventh annual conference on Computational learning theory. Association for Computing Machinery (ACM), pp. 159-164. ISBN 0897916557

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    Abstract

    The best previous algorithm for the matching shoulders lob-pass game, Abe and Takeuchi's (1993) ARTHUR, suffered O(t 1=2 ) regret. We prove that this is the best possible performance for any algorithm that works by accurately estimating the opponent's payoff lines. Then we describe an algorithm which beats that bound and meets the information-theoretic lower bound of O(log t) regret by converging to the best lob rate without accurately estimating the payoff lines. The noise-tolerant binary search procedure that we develop is of independent interest.

    Item Type: Book Section
    Keywords: Matching-Shoulders Lob-Pass Game; Logarithmic Regret; algorithm;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 8136
    Depositing User: Barak Pearlmutter
    Date Deposited: 07 Apr 2017 15:35
    Publisher: Association for Computing Machinery (ACM)
    Refereed: Yes
    URI:

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