Learning implicit information in Bayesian games with knowledge transfer

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	Learning implicit information in Bayesian games with knowledge transfer
	Chen Guanpu; Cao Kai; Hong Yiguang
	2020
发表期刊	Control Theory and Technology
ISSN	2095-6983
卷号	18 期号:3 页码:315-323
摘要	In this paper, we consider to learn the inherent probability distribution of types via knowledge transfer in a two-player repeated Bayesian game, which is a basic model in network security. In the Bayesian game, the attacker's distribution of types is unknown by the defender and the defender aims to reconstruct the distribution with historical actions. It is difficult to calculate the distribution of types directly since the distribution is coupled with a prediction function of the attacker in the game model. Thus, we seek help from an interrelated complete-information game, based on the idea of transfer learning. We provide two different methods to estimate the prediction function in different concrete conditions with knowledge transfer. After obtaining the estimated prediction function, the defender can decouple the inherent distribution and the prediction function in the Bayesian game, and moreover, reconstruct the distribution of the attacker's types. Finally, we give numerical examples to illustrate the effectiveness of our methods.
关键词	Bayesian game repeated game knowledge transfer security
收录类别	CSCD
语种	英语
CSCD记录号	CSCD:6803397
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/57731
专题	中国科学院数学与系统科学研究院
作者单位	中国科学院数学与系统科学研究院
推荐引用方式 GB/T 7714	Chen Guanpu,Cao Kai,Hong Yiguang. Learning implicit information in Bayesian games with knowledge transfer[J]. Control Theory and Technology,2020,18(3):315-323.
APA	Chen Guanpu,Cao Kai,&Hong Yiguang.(2020).Learning implicit information in Bayesian games with knowledge transfer.Control Theory and Technology,18(3),315-323.
MLA	Chen Guanpu,et al."Learning implicit information in Bayesian games with knowledge transfer".Control Theory and Technology 18.3(2020):315-323.