Quantitative Rule-Based Strategy modeling in Classic Indian Rummy: A Metric Optimization Approach

TL;DR

This paper introduces a novel hand-evaluation metric called MinDist for strategic decision-making in Indian Rummy, improving win rates through a computationally efficient, rule-based approach that models opponent behavior and uses statistical evaluation.

Contribution

It presents a new MinDist metric for hand evaluation, along with an efficient algorithm for real-time calculation and opponent modeling within a strategic framework for Indian Rummy.

Findings

MinDist-based agents outperform traditional heuristics in win rates.

The algorithm efficiently computes the metric during gameplay.

Statistical tests confirm the significance of performance improvements.

Abstract

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.