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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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/2/61 |
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| author | Yu-Heng Hsieh Chen-Chun Kao Shyan-Ming Yuan |
| author_facet | Yu-Heng Hsieh Chen-Chun Kao Shyan-Ming Yuan |
| author_sort | Yu-Heng Hsieh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9744850f92e643e98f51ebcd39bbfc94 |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-9744850f92e643e98f51ebcd39bbfc942025-08-20T02:44:40ZengMDPI AGAlgorithms1999-48932025-01-011826110.3390/a18020061Imitating Human Go Players via Vision TransformerYu-Heng Hsieh0Chen-Chun Kao1Shyan-Ming Yuan2Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanDeveloping 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.https://www.mdpi.com/1999-4893/18/2/61Godeep learningVision Transformer |
| spellingShingle | Yu-Heng Hsieh Chen-Chun Kao Shyan-Ming Yuan Imitating Human Go Players via Vision Transformer Algorithms Go deep learning Vision Transformer |
| title | Imitating Human Go Players via Vision Transformer |
| title_full | Imitating Human Go Players via Vision Transformer |
| title_fullStr | Imitating Human Go Players via Vision Transformer |
| title_full_unstemmed | Imitating Human Go Players via Vision Transformer |
| title_short | Imitating Human Go Players via Vision Transformer |
| title_sort | imitating human go players via vision transformer |
| topic | Go deep learning Vision Transformer |
| url | https://www.mdpi.com/1999-4893/18/2/61 |
| work_keys_str_mv | AT yuhenghsieh imitatinghumangoplayersviavisiontransformer AT chenchunkao imitatinghumangoplayersviavisiontransformer AT shyanmingyuan imitatinghumangoplayersviavisiontransformer |