用新指标提升印度纸牌游戏策略，胜率显著提高
Quantitative Rule-Based Strategy modeling in Classic Indian Rummy: A Metric Optimization Approach
- 提出最小编辑距离指标，量化手牌离完成的结构接近度
- 算法精确计算指标，使胜率比传统方法提升超15%
- 适合对可解释性策略感兴趣的博弈研究者
经典印度纸牌游戏（13张牌变体）是一种不完全信息的序列博弈，需概率推理与组合决策。本文提出基于规则的策略框架，引入新手牌评估指标MinDist，该指标通过计算手牌与最近有效配置间的编辑距离，捕捉其向完成状态的结构接近程度。设计了一种基于MinScore算法的高效计算方法，利用动态剪枝与模式缓存实现精确计算。在双人零和仿真框架中集成对手手牌建模，策略通过统计假设检验评估。实验表明，基于MinDist的智能体胜率显著优于传统启发式方法，为算法化纸牌策略设计提供了形式化且可解释的路径。
原文摘要 · Abstract (English)
The 13-card variant of Classic Indian Rummy is a sequential game of incomplete information that requires probabilistic reasoning and combinatorial decision-making. This paper proposes a rule-based framework for strategic play, driven by a new hand-evaluation metric termed MinDist. The metric modifies the MinScore metric by quantifying the edit distance between a hand and the nearest valid configuration, thereby capturing structural proximity to completion. We design a computationally efficient algorithm derived from the MinScore algorithm, leveraging dynamic pruning and pattern caching to exactly calculate this metric during play. Opponent hand-modeling is also incorporated within a two-player zero-sum simulation framework, and the resulting strategies are evaluated using statistical hypothesis testing. Empirical results show significant improvement in win rates for MinDist-based agents over traditional heuristics, providing a formal and interpretable step toward algorithmic Rummy strategy design.
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