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

arXiv: https://arxiv.org/abs/2601.00024 · HTML: https://mlsanity.com/paper/2601.00024/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 6 references that are in the map.
- First posted on arXiv 26 Dec 2025.

## Built on by (most cited first, 1 of 1)

- [IRumAI: Reinforcement Learning for Indian Rummy](https://mlsanity.com/paper/2606.21975/) (2026)

## Abstract (arXiv)

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.