Zweistein: A Dynamic Programming Evaluation Function for Einstein Würfelt Nicht!
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910838990831616 |
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| author | Hsueh, Wei Lin. Hsu, Tsan Sheng. |
| author_facet | Hsueh, Wei Lin. Hsu, Tsan Sheng. |
| contents | This paper introduces Zweistein, a dynamic programming evaluation function for Einstein Würfelt Nicht! (EWN). Instead of relying on human knowledge to craft an evaluation function, Zweistein uses a data-centric approach that eliminates the need for parameter tuning. The idea is to use a vector recording the distance to the corner of all pieces. This distance vector captures the essence of EWN. It not only outperforms many traditional EWN evaluation functions but also won first place in the TCGA 2023 competition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15547 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zweistein: A Dynamic Programming Evaluation Function for Einstein Würfelt Nicht! Hsueh, Wei Lin. Hsu, Tsan Sheng. Artificial Intelligence This paper introduces Zweistein, a dynamic programming evaluation function for Einstein Würfelt Nicht! (EWN). Instead of relying on human knowledge to craft an evaluation function, Zweistein uses a data-centric approach that eliminates the need for parameter tuning. The idea is to use a vector recording the distance to the corner of all pieces. This distance vector captures the essence of EWN. It not only outperforms many traditional EWN evaluation functions but also won first place in the TCGA 2023 competition. |
| title | Zweistein: A Dynamic Programming Evaluation Function for Einstein Würfelt Nicht! |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.15547 |