2021
AAAI
AAAI 2021
A Data-Driven Approach for Gin Rummy Hand Evaluation
Abstract
Abstract 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.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Reinforcement Learning
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Keyword Pioneer
— offensive scoring
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Optimization & Theory > Bayesian Inference
Reinforcement Learning > Applications > Game AI
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Deep Learning > Architectures > Convolutional Neural Networks