用进化算法优化国际象棋经验，让AI在三维象棋中表现更佳
Evolutionary Transfer Learning for Dragonchess
- 从国际象棋引擎迁移启发式函数，再用进化策略优化
- 50轮瑞士制比赛显示优化后性能显著提升
- 适合研究跨域知识迁移的AI开发者参考
Dragonchess是由加里·吉格克斯提出的三维国际象棋变体，具有独特的战略与计算挑战，是研究人工智能启发式知识跨领域迁移的理想环境。本文提出将Dragonchess作为新的AI研究测试平台，并开源基于Python的游戏引擎供社区使用。研究通过直接从领先国际象棋引擎Stockfish迁移启发式评估函数，并利用协方差矩阵自适应进化策略（CMA-ES）进行优化，探索进化迁移学习。初步实验表明，直接迁移效果不佳，因Dragonchess具有多层结构和特殊走法规则。但经进化优化后，智能体性能显著提升，在50轮瑞士制锦标赛中展现出更优对弈表现。该研究验证了进化方法在适应结构复杂、未探索游戏领域中的有效性。
原文摘要 · Abstract (English)
Dragonchess, a three-dimensional chess variant introduced by Gary Gygax, presents unique strategic and computational challenges that make it an ideal environment for studying the transfer of artificial intelligence (AI) heuristics across domains. In this work, we introduce Dragonchess as a novel testbed for AI research and provide an open-source, Python-based game engine for community use. Our research investigates evolutionary transfer learning by adapting heuristic evaluation functions directly from Stockfish, a leading chess engine, and subsequently optimizing them using Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Initial trials showed that direct heuristic transfers were inadequate due to Dragonchess's distinct multi-layer structure and movement rules. However, evolutionary optimization significantly improved AI agent performance, resulting in superior gameplay demonstrated through empirical evaluation in a 50-round Swiss-style tournament. This research establishes the effectiveness of evolutionary methods in adapting heuristic knowledge to structurally complex, previously unexplored game domains.
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