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

  • KataGo
  • Perfect play
  • AlphaZero
  • Game of Go

• 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.