{"ID":2825084,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00024","arxiv_id":"2601.00024","title":"Quantitative Rule-Based Strategy modeling in Classic Indian Rummy: A Metric Optimization Approach","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.","short_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 metri...","url_abs":"https://arxiv.org/abs/2601.00024","url_pdf":"https://arxiv.org/pdf/2601.00024v1","authors":"[\"Purushottam Saha\",\"Avirup Chakraborty\",\"Sourish Sarkar\",\"Subhamoy Maitra\",\"Diganta Mukherjee\",\"Tridib Mukherjee\"]","published":"2025-12-26T21:03:47Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.GT\"]","methods":"[]","has_code":false}