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Analyzing KataGo: A Comparative Evaluation Against Perfect Play in the Game of Go
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- Author / Creator
- Husna, Asmaul
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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
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- Graduation date
- Fall 2024
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- 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.