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An Adversarial Search Method Based on an Iterative Optimal Strategy

Author

Listed:
  • Chanjuan Liu

    (School of Computer Science and Technology, Dalian University of Technology; Dalian 116024, China)

  • Junming Yan

    (CNGC North Automatic Control Technology Institute, Taiyuan 030000, China)

  • Yuanye Ma

    (School of Computer Science and Technology, Dalian University of Technology; Dalian 116024, China)

  • Tianhao Zhao

    (School of Computer Science and Technology, Dalian University of Technology; Dalian 116024, China)

  • Qiang Zhang

    (School of Computer Science and Technology, Dalian University of Technology; Dalian 116024, China)

  • Xiaopeng Wei

    (School of Computer Science and Technology, Dalian University of Technology; Dalian 116024, China)

Abstract

A deeper game-tree search can yield a higher decision quality in a heuristic minimax algorithm. However, exceptions can occur as a result of pathological nodes, which are considered to exist in all game trees and can cause a deeper game-tree search, resulting in worse play. To reduce the impact of pathological nodes on the search quality, we propose an iterative optimal minimax (IOM) algorithm by optimizing the backup rule of the classic minimax algorithm. The main idea is that calculating the state values of the intermediate nodes involves not only the static evaluation function involved but also a search into the future, where the latter is given a higher weight. We experimentally demonstrated that the proposed IOM algorithm improved game-playing performance compared to the existing algorithms.

Suggested Citation

  • Chanjuan Liu & Junming Yan & Yuanye Ma & Tianhao Zhao & Qiang Zhang & Xiaopeng Wei, 2020. "An Adversarial Search Method Based on an Iterative Optimal Strategy," Mathematics, MDPI, vol. 8(9), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1623-:d:416078
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    References listed on IDEAS

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    1. John C. Harsanyi & Reinhard Selten, 1988. "A General Theory of Equilibrium Selection in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262582384, December.
    2. 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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