A Heuristic Evaluation Function for Hand Strength Estimation in Gin Rummy

Aqib Ahmed, Joshua Leppo, Michal Lesniewski, Riken Patel, Jonathan Perez, Jeremy J. Blum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes a fast hand strength estimation model for the game of Gin Rummy. The algorithm is computationally inexpensive, and it incorporates not only cards in the player's hand but also cards known to be in the opponent's hand, cards in the discard pile, and the current game stage. This algorithm is used in conjunction with counterfactual regret (CFR) minimization to develop a gin rummy bot. CFR strategies were developed for the knocking strategies. The hand strength estimation algorithm was used to select a discard that balances the goals of maximizing the utility of the player's hand and minimizing the likelihood that a card will be useful to the opponent. A study of the parameterization of this estimation algorithm demonstrates the soundness of approach as well as good performance under a wide range of parameter values.

Original languageEnglish (US)
Title of host publication35th AAAI Conference on Artificial Intelligence, AAAI 2021
PublisherAssociation for the Advancement of Artificial Intelligence
Pages15465-15471
Number of pages7
ISBN (Electronic)9781713835974
StatePublished - 2021
Event35th AAAI Conference on Artificial Intelligence, AAAI 2021 - Virtual, Online
Duration: Feb 2 2021Feb 9 2021

Publication series

Name35th AAAI Conference on Artificial Intelligence, AAAI 2021
Volume17B

Conference

Conference35th AAAI Conference on Artificial Intelligence, AAAI 2021
CityVirtual, Online
Period2/2/212/9/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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