Imitating Human Go Players via Vision Transformer
Developing AI algorithms for the game of Go has long been a challenging task. While tools such as AlphaGo have revolutionized gameplay, their focus on maximizing win rates often leads to moves that are incomprehensible to human players, limiting their utility as training aids. This work introduces a...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/18/2/61 |
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| Summary: | Developing AI algorithms for the game of Go has long been a challenging task. While tools such as AlphaGo have revolutionized gameplay, their focus on maximizing win rates often leads to moves that are incomprehensible to human players, limiting their utility as training aids. This work introduces a novel approach to bridge this gap by leveraging a Vision Transformer (ViT) to develop an AI model that achieves professional-level play while mimicking human decision-making. Using a dataset from the KGS Go server, our ViT-based model achieves 51.49% accuracy in predicting expert moves with a simple feature set. Comparative analysis against CNN-based models highlights the ViT’s superior performance in capturing patterns and replicating expert strategies. These findings establish ViTs as promising tools for enhancing Go training by aligning AI strategies with human intuition. |
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| ISSN: | 1999-4893 |