PRELIMINARY PREPRINT VERSION: DO NOT CITE The AAAI Digital Library will contain the published version some time after the conference. Opponent Hand Estimation in the Game of Gin Rummy Peter E. Francis, Hoang A. Just, Todd W. Neller Gettysburg College ffranpe02, justho01,
[email protected] Abstract can make a difference in a game play strategy. We conclude In this article, we describe various approaches to oppo- with a demonstration of a simple deterministic application of nent hand estimation in the card game Gin Rummy. We our hand estimation that produces a statistically significant use an application of Bayes’ rule, as well as both sim- advantage for a player. ple and convolutional neural networks, to recognize pat- terns in simulated game play and predict the opponent’s Gin Rummy hand. We also present a new minimal-sized construction for using arrays to pre-populate hand representation im- Gin Rummy is one of the most popular 2-player card games ages. Finally, we define various metrics for evaluating played with a standard (a.k.a. French) 52-card deck. Ranks estimations, and evaluate the strengths of our different run from aces low to kings high. The object of the game is to estimations at different stages of the game. be the first player to score 100 or more points accumulated through the scoring of individual hands. Introduction The play of Gin Rummy, as with other games in the In this work, we focus on different computational strate- Rummy family, is to collect sets of cards called melds. gies to estimate the opponent’s hand in the card game Gin There are two types of melds: “sets” and “runs”.