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Analyzing KataGo: A Comparative Evaluation Against Perfect Play in the Game of Go

  • Author / Creator
    Husna, Asmaul
  • Research on board games focuses on playing at a superhuman level or finding
    exact solutions. Recently, Artificial Intelligence (AI) has become really good
    at playing complex games such as Go. Comparing AI systems to perfect play
    helps us understand how advanced AI has become. This research explores the
    performance of KataGo, an AlphaZero-like program, in the game of Go. Our
    study investigates how different neural networks and search strategies impact
    KataGo’s decision-making abilities when compared against perfect play. In our
    research, we develop a larger Go endgame dataset labelled with perfect solutions,
    and examine KataGo’s strengths and weaknesses through experiments
    and analysis. We observe the effectiveness of strong policies in improving
    move selection, the benefits and demerits of MCTS search enhancements, and
    the challenges KataGo faces in competing against an exact solver. We further
    analyse move choices by showing the changes of average action values,
    lower confidence bound (lcb), winrate, and number of visited node according
    to MCTS search in KataGo. KataGo has a 90.8% success rate while playing
    matches against an exact solver in the perfect game dataset.

  • Subjects / Keywords
  • Graduation date
    Fall 2024
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-e6qx-hs52
  • License
    This thesis is made available by the University of Alberta Library 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.