@article{Truong_Neller_2021, title={A Data-Driven Approach for Gin Rummy Hand Evaluation}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/17843}, DOI={10.1609/aaai.v35i17.17843}, abstractNote={We develop a data-driven approach for hand strength evaluation in the game of Gin Rummy. Employing Convolutional Neural Networks, Monte Carlo simulation, and Bayesian reasoning, we compute both offensive and defensive scores of a game state. After only one training cycle, the model was able to make sophisticated and human-like decisions with a 55.4% +/- 0.8% win rate (90% confidence level) against a Simple player.}, number={17}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Truong, Sang T. and Neller, Todd W.}, year={2021}, month={May}, pages={15647-15654} }