Opponent Hand Estimation in the Game of Gin Rummy

Peter E. Francis, Hoang A. Just, Todd W. Neller

AAAI 2021 pp. 15496-15502

doi:10.1609/AAAI.V35I17.17824 /aaai/2021/francis2021aaai-opponent/

Abstract

In this article, we describe various approaches to opponent hand estimation in the card game Gin Rummy. We use an application of Bayes' rule, as well as both simple and convolutional neural networks, to recognize patterns in simulated game play and predict the opponent's hand. We also present a new minimal-sized construction for using arrays to pre-populate hand representation images. Finally, we define various metrics for evaluating estimations, and evaluate the strengths of our different estimations at different stages of the game.

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Cite

Text

Francis et al. "Opponent Hand Estimation in the Game of Gin Rummy." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I17.17824

Markdown

[Francis et al. "Opponent Hand Estimation in the Game of Gin Rummy." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/francis2021aaai-opponent/) doi:10.1609/AAAI.V35I17.17824

BibTeX

@inproceedings{francis2021aaai-opponent,
  title     = {{Opponent Hand Estimation in the Game of Gin Rummy}},
  author    = {Francis, Peter E. and Just, Hoang A. and Neller, Todd W.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15496-15502},
  doi       = {10.1609/AAAI.V35I17.17824},
  url       = {https://mlanthology.org/aaai/2021/francis2021aaai-opponent/}
}