Lob-pass problem

Jun Ichi Takeuchi, Naoki Abe, Shun Ichi Amari

Research output: Contribution to journalConference article

Abstract

We consider a new variant of the online learning model in which the goal of an agent is to choose his or her actions so as to maximize the number of successes, while learning about his or her reacting environment through those very actions. In particular, we consider a model of tennis play, in which the only actions that the player can take are a pass and a lob, and the opponent is modeled by two linear (probabilistic) functions fL(r) = a1r+b1 and fP(r) = a2r+b2, specifying the probability that a lob (and a pass, respectively) will win a point when the proportion of lobs played in the past trials is r. We measure the performance of a player in this model by his or her expected regret, namely how many fewer points the player expects to win as compared to the ideal player (one that knows the two probabilistic functions) as a function of t, the total number of trials, which is unknown to the player a priori. Assuming that the probabilistic functions satisfy the `matching shoulders condition,' i.e., fL(0) = fP(1), we obtain a variety of upper bounds for assumptions and restrictions of varying degrees, ranging from O(log t), O(t1/2), O(t3/5), O(t2/3) to O(t5/7) as well as a matching lower bound of order Ω(log t) for the first case. When the total number of trials t is given to the player in advance, the upper bounds can be improved significantly. An extended abstract describing part of this work has appeared in N. Abe and J. Takeuchi, 1993, in `Proceedings of the Sixth Annual ACM Workshop on Computational Learning Theory,' pp. 422-428.

Original languageEnglish
Pages (from-to)523-557
Number of pages35
JournalJournal of Computer and System Sciences
Volume61
Issue number3
DOIs
Publication statusPublished - Dec 2000
Externally publishedYes
Event17th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems - Seattle, WA, USA
Duration: Jun 1 1998Jun 4 1998

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Networks and Communications
  • Computational Theory and Mathematics
  • Applied Mathematics

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