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High-Precision Estimation of the State-Space Complexity of Shogi via the Monte Carlo Method

ArXiv CS.AI5h ago
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Researchers have developed a high-precision statistical approach to estimate the state-space complexity of Shogi, resolving a decades-old puzzle that had a five-order-of-magnitude uncertainty. This breakthrough is significant for AI game-playing research, as accurately understanding game complexity helps inform algorithm design and computational requirements for developing stronger game-playing AI systems.

Scientists narrow Shogi's complexity gap using Monte Carlo statistical methods.

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