Summary of Solving 7×7 Killall-go with Seki Database, by Yun-jui Tsai et al.
Solving 7×7 Killall-Go with Seki Database
by Yun-Jui Tsai, Ting Han Wei, Chi-Huang Lin, Chung-Chin Shih, Hung Guei, I-Chen Wu, Ti-Rong Wu
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an innovative technique for reducing the search space in 7×7 Killall-Go games by recognizing mutual life patterns, also known as seki. In Go-like games, live patterns are crucial to protect stones from opponent capture, and seki occurs when both players’ stones achieve life by sharing liberties with their opponent. By enumerating and storing all seki patterns up to a predetermined area size in a seki table, the proposed method significantly improves solving efficiency for Killall-Go. Experimental results show that this approach can solve unsolvable positions in under 15 minutes, achieving a 10% to 20% improvement in wall clock time and node count compared to general solvers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers play Go-like games better by recognizing patterns that are important for winning. In these games, certain stone arrangements can be “alive” because they are protected from being captured by their opponent. When both players’ stones are alive because of each other, this is called a “mutual life” or “seki.” Recognizing seki patterns allows computers to search more efficiently and make better moves. The authors of this paper created a special table that contains all the possible seki patterns up to a certain size. This helps computers solve puzzles faster and makes them better at playing these games. |
