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Move Groups as a General Enhancement for Monte Carlo Tree Search

  • Author / Creator
    Van Eyck, Gabriel
  • Monte Carlo tree search (MCTS) combined with the upper confidence bounds applied to trees (UCT) algorithm has brought forth many advances in game related AI. This includes general game players and programs for specific games such as Amazons, Arimaa, and Go. However, there often is a need for further enhancements beyond this to make the strongest player for a given game. Move groups are investigated in detail to determine their effect on MCTS as a general enhancement. The structures of move groups that increase performance in an artificial game are analyzed then applied without knowledge of move values. We noted that there is a speed increase in the tree while maintaining performance for a fixed number of simulations. Finally, move groups are applied to the game of Amazons successfully.

  • Subjects / Keywords
  • Graduation date
    2014-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3513V420
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Mueller, Martin (Computing Science)
  • Examining committee members and their departments
    • Mueller, Martin (Computing Science)
    • Buro, Michael (Computing Science)
    • Mackenzie, Marc (Oncology)