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Improving Strategic Decisions in Sequential Games by Exploiting Positional Similarity

Author

Listed:
  • Sabrina Evans

    (Department of Mathematics, Yale University, New Haven, CT 06511, USA
    Bloop AI, London WC1H 9SE, UK)

  • Paolo Turrini

    (Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK)

Abstract

We study the strategic similarity of game positions in two-player extensive games of perfect information by looking at the structure of their local game trees, with the aim of improving the performance of game-playing agents in detecting forcing continuations. We present a range of measures over the induced game trees and compare them against benchmark problems in chess, observing a promising level of accuracy in matching up trap states. Our results can be applied to chess-like interactions where forcing moves play a role, such as those arising in bargaining and negotiation.

Suggested Citation

  • Sabrina Evans & Paolo Turrini, 2023. "Improving Strategic Decisions in Sequential Games by Exploiting Positional Similarity," Games, MDPI, vol. 14(3), pages 1-13, April.
    • Handle: RePEc:gam:jgames:v:14:y:2023:i:3:p:36-:d:1135043
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    References listed on IDEAS

    as
    1. Schwalbe, Ulrich & Walker, Paul, 2001. "Zermelo and the Early History of Game Theory," Games and Economic Behavior, Elsevier, vol. 34(1), pages 123-137, January.
    2. Ariel Rubinstein, 1997. "Modeling Bounded Rationality," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262681005, April.
    3. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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