发表机构
Connecticut College（康涅狄格学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在三维国际象棋龙棋中比较进化迁移学习与TD(λ)两种自适应方法，通过C++重写引擎并运行万局游戏，证明二者均优于其他智能体且性能无显著差异，验证了自适应方法在复杂新领域的有效性。
AI 中文摘要
我们的研究探讨了两种自适应人工智能方法——进化迁移学习和TD(λ)——在三维国际象棋环境龙棋中的表现。该游戏以其独特的棋盘结构和计算负载给玩家带来挑战，使其成为研究自适应方法如何在新型环境中更新评估启发式函数的理想场景。在这项工作中，我们重新实现了龙棋引擎，将其从PyGame引擎改为C++。这实现了更快的游戏速度，使我们能够运行10,000局游戏并进行置信区间和显著性检验，而非仅进行一场小型锦标赛。两种自适应方法在循环赛中都优于所有其他智能体。我们的结果表明，进化评估与学习评估之间的性能没有显著差异。这项研究确立了自适应方法在结构复杂、新颖的游戏领域中的有效性。
英文摘要
Our research investigates how two adaptive AI methods, evolutionary transfer learning and TD(lambda), perform in the three-dimensional chess environment Dragonchess. The game challenges players with its unique board structure and computational load, making it an ideal setting to study how adaptive methods can update evaluation heuristics in novel environments. In this work we re-implement the Dragonchess engine, changing it from a PyGame engine to C++. This enables faster gameplay, allowing us to run 10,000 games with confidence intervals and significance tests, rather than a single small tournament. Both adaptive methods outperform all other agents in the round-robin tournament. Our results showed that there is no significant difference in the performance between the evolved and learned evaluations. This research establishes the efficacy of adaptive methods in structurally complex, novel game domains.
CommentsSpringer Lecture Notes in Artificial Intelligence
