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2021 AAAI AAAI 2021

A Highly-Parameterized Ensemble to Play Gin Rummy

Abstract

Abstract This paper describes the design and training of a computer Gin Rummy player. The system includes three main components to make decisions about drawing cards, discarding, and ending the game, with numerous parameters controlling behavior. In particular, an ensemble approach is explored in the discard decision. Finally, three sets of parameter tuning and performance experiments are analyzed.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning and Reinforcement Learning
🧭 Keyword Pioneer — discard decision
🐝 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

Masayuki Nagai , Kavya Shrivastava , Kien Ta , Steven Bogaerts , Chad Byers

Topics

Artificial Intelligence > Core AI > Game AI Machine Learning > Core Methods > Classification Reinforcement Learning > Applications > Game AI Deep Learning > Learning Types > Ensemble Learning Machine Learning > Learning Types > Hyperparameter Optimization

Keywords

ensemble learning game playing parameter tuning ensemble method neural network gin rummy discard decision
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