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New Self-Play Results in Computer Chess

  • Ernst A. Heinz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2063)

Abstract

This paper presents the results of a new self-play experiment in computer chess. It is the first such experiment ever to feature search depths beyond 9 plies and thousands of games for every single match. Overall, we executed 24,000 self-play games (3,000 per match) in one “calibration” match and seven “depth X+1⇔X” handicap matches at fixed iteration depths ranging from 5–12 plies. For the experiment to be realistic and independently repeatable, we relied on a state-of-the-art commercial contestant: Fritz 6, one of the strongest modern chess programs available. The main result of our new experiment is that it shows the existence of diminishing returns for additional search in computer chess selfplay by Fritz 6 with 95% statistical confidence. The average rating gain per search iteration shrinks by half from 169 ELO at 6 plies to 84 ELO at 12 plies. The diminishing returns manifest themselves by declining rates of won games and reversely increasing rates of drawn games for the deeper searching program versions. Their rates of lost games, however, remain quite steady for the whole depth range of 5–12 plies.

Keywords

diminishing returns search vs. knowledge self-play 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ernst A. Heinz
    • 1
  1. 1.M.I.T. Laboratory for Computer Science (Room NE 43-228)Massachussetts Institute of TechnologyCambridgeUSA

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