融合传统谋略博弈思想的轻量化围棋语音仿真交互系统研究

Research on Lightweight Go Voice Simulation Interactive System Integrating Traditional Strategic Game Thinking

摘要: 针对当前围棋仿真AI存在决策不可解释、语音交互冲突、复盘缺乏量化标准等缺陷，本文结合古代传统谋略博弈思想，开发轻量化十路围棋可视化仿真系统。该系统提取孤棋、子力差等多维度棋盘特征，构建轻量决策树映射模型替代原有if-then固定阈值规则，建立攻防态势与传统谋略战术自动映射体系，实现对局实时战术语音解读；基于启发式规则融合轻量化MCTS蒙特卡洛树搜索搭建三层分级博弈智能体，搭载云端模型下载与本地策略降级容错机制；搭建云端、本地双语音同步调度模块，通过互斥锁与缓存队列解决重复播报、落子阻塞问题；构建加权评分模型，从多维度生成标准化对局复盘报告。基于Python-Pygame完成整套可视化界面开发，集成难度切换、语音控制、悔棋、一键复盘等交互功能。多组仿真实验结果表明，系统战术匹配识别准确率达92.7%，三级AI残局对抗性能相较随机、贪心基准模型分别提升31.4%、24.6%，语音交互时延稳定低于350 ms，全程无交互故障。该系统将传统谋略博弈逻辑与轻量化棋类仿真结合，弥补传统围棋AI黑盒短板，完善零和博弈可解释性理论，可为棋类教学、轻量化兵棋推演提供完整的工程实现方案。

Abstract: In response to the shortcomings of current Go simulation AI, such as inexplicable decision-making, voice interaction conflicts, and lack of quantitative standards for replay, this article combines ancient traditional strategic game thinking to develop a lightweight ten-way Go visualization simulation system. The system extracts multi-dimensional chessboard features such as lone chess and sub force difference, constructs a lightweight decision tree mapping model to replace the original if-then fixed threshold rules, establishes an automatic mapping system between offensive and defensive situations and traditional strategies and tactics, and achieves real-time tactical speech interpretation of the game; builds a three-layer hierarchical game agent based on heuristic rule fusion and lightweight MCTS Monte Carlo tree search, equipped with cloud model download and local strategy downgrade fault-tolerant mechanism; builds a cloud and local dual voice synchronization scheduling module to solve the problems of duplicate broadcasting and drop blocking through mutex locks and cache queues; builds a weighted scoring model to generate standardized review reports from multiple dimensions. The development of a complete visual interface based on Python-Pygame is completed, integrating interactive functions such as difficulty switching, voice control, regret chess, and one-click review. The results of multiple simulation experiments show that the tactical matching recognition accuracy of the system reaches 92.7%, and the performance of the three-level AI residual adversarial model is improved by 31.4% and 24.6% compared to the random and greedy benchmark models, respectively. The voice interaction delay is stable below 350 ms, and there are no interaction faults throughout the process. This system combines traditional strategic game logic with lightweight chess simulation to address the shortcomings of traditional Go AI black box and improve the interpretability theory of zero-sum games. It can provide a complete engineering implementation solution for chess teaching and lightweight chess deduction.