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.

翻译：经典印度拉米纸牌13张变体是一种不完全信息的序贯博弈，需要概率推理与组合决策。本文提出一种基于规则的策略性出牌框架，其核心驱动力是一种称为MinDist的新型手牌评估度量。该度量通过量化手牌与最近有效配置之间的编辑距离来改进MinScore度量，从而捕捉手牌接近完成的结构性邻近度。我们设计了一种计算高效的算法，该算法源自MinScore算法，利用动态剪枝与模式缓存技术，可在游戏过程中精确计算此度量。对手手牌建模亦被纳入一个双人零和模拟框架内，所得策略通过统计假设检验进行评估。实证结果表明，基于MinDist的智能体相较于传统启发式方法在胜率上取得显著提升，为算法化拉米策略设计迈出了形式化且可解释的一步。