This paper presents a new approach that integrates deep learning with
computational chess, using both the Mixture of Experts (MoE) method and
Monte-Carlo Tree Search (MCTS). Our methodology employs a suite of specialized
models, each designed to respond to specific changes in the game's input data.
This results in a framework with sparsely activated models, which provides
significant computational benefits. Our framework combines the MoE method with
MCTS, in order to align it with the strategic phases of chess, thus departing
from the conventional ``one-for-all'' model. Instead, we utilize distinct game
phase definitions to effectively distribute computational tasks across multiple
expert neural networks. Our empirical research shows a substantial improvement
in playing strength, surpassing the traditional single-model framework. This
validates the efficacy of our integrated approach and highlights the potential
of incorporating expert knowledge and strategic principles into neural network
design. The fusion of MoE and MCTS offers a promising avenue for advancing
machine learning architectures.

本研究采用深度学习与计算博弈相结合的新方法，通过 Mixture of Experts（MoE）方法和 Monte-Carlo Tree Search（MCTS）来提高计算机棋类游戏的表现，实验结果显示相较于传统单一模型，该综合方法显著提升了游戏的强度，验证了专家知识与策略原则融入神经网络设计的潜力。