Quantitative Rule-Based Strategy modeling in Classic Indian Rummy: A Metric Optimization Approach
This work addresses algorithmic strategy design for the domain-specific game of Classic Indian Rummy, representing an incremental advance with formal and interpretable methods.
The paper tackled the problem of strategic play in Classic Indian Rummy by proposing a rule-based framework using a new hand-evaluation metric called MinDist, which quantifies edit distance to completion, and showed significant improvement in win rates over traditional heuristics.
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.