Relevance-Zone Reduction in Game Solving
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arXiv
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| Format: | Preprint |
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2025
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| author | Lin, Chi-Huang Wei, Ting Han Wang, Chun-Jui Guei, Hung Shih, Chung-Chin Tsai, Yun-Jui Wu, I-Chen Wu, Ti-Rong |
| author_facet | Lin, Chi-Huang Wei, Ting Han Wang, Chun-Jui Guei, Hung Shih, Chung-Chin Tsai, Yun-Jui Wu, I-Chen Wu, Ti-Rong |
| contents | Game solving aims to find the optimal strategies for all players and determine the theoretical outcome of a game. However, due to the exponential growth of game trees, many games remain unsolved, even though methods like AlphaZero have demonstrated super-human level in game playing. The Relevance-Zone (RZ) is a local strategy reuse technique that restricts the search to only the regions relevant to the outcome, significantly reducing the search space. However, RZs are not unique. Different solutions may result in RZs of varying sizes. Smaller RZs are generally more favorable, as they increase the chance of reuse and improve pruning efficiency. To this end, we propose an iterative RZ reduction method that repeatedly solves the same position while gradually restricting the region involved, guiding the solver toward smaller RZs. We design three constraint generation strategies and integrate an RZ Pattern Table to fully leverage past solutions. In experiments on 7x7 Killall-Go, our method reduces the average RZ size to 85.95% of the original. Furthermore, the reduced RZs can be permanently stored as reusable knowledge for future solving tasks, especially for larger board sizes or different openings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00689 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Relevance-Zone Reduction in Game Solving Lin, Chi-Huang Wei, Ting Han Wang, Chun-Jui Guei, Hung Shih, Chung-Chin Tsai, Yun-Jui Wu, I-Chen Wu, Ti-Rong Artificial Intelligence Game solving aims to find the optimal strategies for all players and determine the theoretical outcome of a game. However, due to the exponential growth of game trees, many games remain unsolved, even though methods like AlphaZero have demonstrated super-human level in game playing. The Relevance-Zone (RZ) is a local strategy reuse technique that restricts the search to only the regions relevant to the outcome, significantly reducing the search space. However, RZs are not unique. Different solutions may result in RZs of varying sizes. Smaller RZs are generally more favorable, as they increase the chance of reuse and improve pruning efficiency. To this end, we propose an iterative RZ reduction method that repeatedly solves the same position while gradually restricting the region involved, guiding the solver toward smaller RZs. We design three constraint generation strategies and integrate an RZ Pattern Table to fully leverage past solutions. In experiments on 7x7 Killall-Go, our method reduces the average RZ size to 85.95% of the original. Furthermore, the reduced RZs can be permanently stored as reusable knowledge for future solving tasks, especially for larger board sizes or different openings. |
| title | Relevance-Zone Reduction in Game Solving |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.00689 |