TY - JOUR AU - Truong, Sang T. AU - Neller, Todd W. PY - 2021/05/18 Y2 - 2024/03/28 TI - A Data-Driven Approach for Gin Rummy Hand Evaluation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 17 SE - EAAI Symposium: Full Papers DO - 10.1609/aaai.v35i17.17843 UR - https://ojs.aaai.org/index.php/AAAI/article/view/17843 SP - 15647-15654 AB - 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. ER -