Abstract
Inference commonly happens in our daily lives and is also a hot topic for AI research. In this paper, we infer move sequences in Go, i.e., the order in which moves are played, from stone arrangements on the board. We formulate the problem as likelihood maximization and employ a general optimization algorithm, simulated annealing, to solve it. Our experiments on professional and amateur games show that the proposed approach sometimes produces more natural move sequences than those played by humans.
This work was supported by JSPS KAKENHI Grant Numbers JP23K17021 and JP23K11381.
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Notes
- 1.
In some cases, players may intentionally play unexpected moves (e.g., to transpose to openings that are not well-studied so that the opponent may make mistakes). Such cases are not the inference targets in this paper.
- 2.
https://sjeng.org/zero/best_v1.txt.zip from the Leela Zero project, https://github.com/leela-zero/leela-zero.
- 3.
Professional: https://mega.co.nz/#!xFE2kTaK!Oj3_N9NpGmYVGTuka7Nc3T0HTmp3kKcXZR6p1Q7U5YU, amateur (1d): https://github.com/featurecat/go-dataset.
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Hsueh, CH., Ikeda, K. (2024). Can We Infer Move Sequences in Go from Stone Arrangements?. In: Hartisch, M., Hsueh, CH., Schaeffer, J. (eds) Advances in Computer Games. ACG 2023. Lecture Notes in Computer Science, vol 14528. Springer, Cham. https://doi.org/10.1007/978-3-031-54968-7_7
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