DocumentCode :
2657638
Title :
Machine Learning in Adversarial Game Using Flight Chess
Author :
Liu, Yu ; Li, Dan ; Hu, Yingsong
Author_Institution :
Coll. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China
fYear :
2011
fDate :
4-6 Nov. 2011
Firstpage :
65
Lastpage :
68
Abstract :
Game playing is a perfect domain of the study of machine learning for its simplicity that allows the researchers to focus on the learning problems themselves and ignore marginal factors. Many learning techniques derived from games have been applied successfully in other learning problems. In this paper, we introduce a Minimax Recurrence Learning algorithm to reinforce the intelligence of a game agent and a supervised learning technique to train the agent. It proves that our intelligent flight chess agent defeat human players in the flight chess game with high probability. Theory deduction proves that combination of the reinforcement learning and supervised learning techniques used in our agent can learn the essential knowledge in an adversarial game. The infrastructure and the algorithm of our agent can be extended in other learning problems also.
Keywords :
computer games; learning (artificial intelligence); adversarial game; flight chess; machine learning; minimax recurrence learning algorithm; reinforcement learning; supervised learning technique; Approximation methods; Games; Learning; Machine learning; Supervised learning; Training; Transfer functions; feature characterization; machine learning; reinforcement learning; supervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Information Networking and Security (MINES), 2011 Third International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4577-1795-6
Type :
conf
DOI :
10.1109/MINES.2011.124
Filename :
6103723
Link To Document :
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