A Data-Driven Approach for Gin Rummy Hand Evaluation

No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

EAAI Symposium: Full Papers

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.

10.1609/aaai.v35i17.17843