Learning implicit information in Bayesian games with knowledge transfer
Guanpu CHEN1
Kai CAO2
Yiguang HONG3
1.Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China2.School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China3.Academy of Mathematics and Systems Science, CAS
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.
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论文发表日期：2020-08-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：9( 315-323 )
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