DocumentCode
423873
Title
Using Bayesian networks to model the belief in the opponent in static game with incomplete information
Author
Wang, Xiao-Feng ; Liu, Wei-Yi ; Li, Jin ; Zhao, Yun
Author_Institution
Dept. of Comput. Sci., Yunnan Univ., China
Volume
1
fYear
2004
fDate
26-29 Aug. 2004
Firstpage
249
Abstract
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 decisions 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.
Keywords
belief networks; game theory; Bayesian networks; noncooperative game theory; static game; Artificial intelligence; Bayesian methods; Computer science; Distributed computing; Game theory; Information analysis; Intelligent networks; Light rail systems; Probability distribution; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN
0-7803-8403-2
Type
conf
DOI
10.1109/ICMLC.2004.1380669
Filename
1380669
Link To Document