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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.00024 |
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| _version_ | 1866914229687156736 |
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| author | Saha, Purushottam Chakraborty, Avirup Sarkar, Sourish Maitra, Subhamoy Mukherjee, Diganta Mukherjee, Tridib |
| author_facet | Saha, Purushottam Chakraborty, Avirup Sarkar, Sourish Maitra, Subhamoy Mukherjee, Diganta Mukherjee, Tridib |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00024 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantitative Rule-Based Strategy modeling in Classic Indian Rummy: A Metric Optimization Approach Saha, Purushottam Chakraborty, Avirup Sarkar, Sourish Maitra, Subhamoy Mukherjee, Diganta Mukherjee, Tridib Artificial Intelligence Computer Science and Game Theory 91(Primary), 05(Secondary) 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. |
| title | Quantitative Rule-Based Strategy modeling in Classic Indian Rummy: A Metric Optimization Approach |
| topic | Artificial Intelligence Computer Science and Game Theory 91(Primary), 05(Secondary) |
| url | https://arxiv.org/abs/2601.00024 |