Using Bayesian networks to model the belief in the opponent in static game with incomplete information

被引：0
|
作者：
Wang, XF [1 ]
Liu, WY [1 ]
Li, J [1 ]
Zhao, Y [1 ]
机构：
[1] Yunnan Univ, Dept Comp Sci, Kunming, Peoples R China
来源：
PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2004年
关键词：
game theory; equilibria; strategy; Bayesian Networks; belief;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Noncooperative game theory provides a normative framework for analyzing strategic interactions of agents. In some noncooperative games agent may be lack of information about its opponents. So it must make decision on uncertain opponents. In this paper, Bayesian Network is used to model the agent uncertainty Of its opponents. The uncertainty can be updated when some events happen through Bayesian Network.
引用
收藏
页码：249 / 252
页数：4
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