2007 IEEE International Conference on Granular Computing (GRC 2007) SRML Learning Game Theory with Application to Internet Security and Management Systems San Jose, California November 02-November 04 ISBN: 0-7695-3032-X
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/GrC.2007.157
On May 8, 1997 IBM's Deep Blue computer chess pro- gram had beaten chess grand master G. Kasparov in New York. On August 10, 2006 computer Chinese chess sys- tems had also beaten grand masters marginally in Beijing. Both types of chess game systems are planned searching ex- pert computer systems without machine learning capabil- ity. However computer GO game systems are still far be- hind human GO masters's capability. Therefore a machine learning game theory could be still important research in game theory. In this article a SRM machine learning game theory is introduced. The application of our game theory to internet security, computer security, GO games, robotics, and management systems will be investigated. The general application of our game theory to business, economics, en- gineering, social science, and other related fields are also discussed.
Citation:
James Kuodo Huang, Bang-su Chen, "SRML Learning Game Theory with Application to Internet Security and Management Systems," grc, pp.584, 2007 IEEE International Conference on Granular Computing (GRC 2007), 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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