Belief-Guided Decision Making with Uncertainty Gating in the Game of Go· 信念引导的围棋决策
Recent advancements in Computer Go, driven by AlphaZero and MuZero, rely heavily on Monte Carlo Tree Search (MCTS) to correct the errors of the neural network policy. While effective on massive computational clusters, this dependence creates a critical bottleneck on consumer-grade hardware, where the computational cost of tree management severely limits inference rates. Furthermore, without deep search, these models suffer from hallucination, proposing moves with high confidence that are strategically fatal. This paper introduces a novel Belief-Guided architecture that disentangles the Policy head from a distinct Belief head. Unlike traditional value functions, the Belief head acts as an internal simulator and independent critic, modeling epistemic uncertainty and strategic stability. By i
介绍一种新型信念引导的围棋决策架构，解决神经网络策略错误和计算资源限制问题。
- 核心方法
- 提出Belief-Guided架构，将策略头与信念头分离，信念头作为内部模拟器和独立评估者，建模认知不确定性及战略稳定性。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有围棋AI依赖大规模计算资源进行MCTS以修正神经网络策略错误，且在计算资源受限时易出现策略失误。
- 关键实验
- 通过与传统模型对比实验验证了模型在减少计算成本和提高策略准确性方面的效果。
- 主要贡献
- 减少了对计算资源的依赖，提高了模型在消费者级硬件上的性能；降低了策略失误的概率。
- 意义与局限
- 该研究为在有限计算资源下部署复杂AI模型提供了新的思路，但未明确其在顶级竞赛中的表现。