SRML学习博弈论及其在互联网安全和管理系统中的应用

James Kuodo Huang, Bang-su Chen
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引用次数: 1

摘要

1997年5月8日,IBM的“深蓝”计算机国际象棋程序在纽约击败了国际象棋大师G.卡斯帕罗夫。2006年8月10日,中国计算机象棋系统也在北京以微弱优势击败了象棋大师。这两种棋类系统都是计划搜索的专家计算机系统,没有机器学习能力。然而,计算机围棋系统仍然远远落后于人类围棋高手的能力。因此,机器学习博弈论仍然是博弈论的重要研究方向。本文介绍了一种SRM机器学习博弈论。我们的博弈论在互联网安全、计算机安全、围棋游戏、机器人和管理系统中的应用将被调查。我们的博弈论在商业、经济、工程、社会科学和其他相关领域的一般应用也进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SRML Learning Game Theory with Application to Internet Security and Management Systems
On May 8, 1997 IBM's Deep Blue computer chess program had beaten chess grand master G. Kasparov in New York. On August 10, 2006 computer Chinese chess systems had also beaten grand masters marginally in Beijing. Both types of chess game systems are planned searching expert computer systems without machine learning capability. However computer GO game systems are still far behind 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, engineering, social science, and other related fields are also discussed.
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