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- 저자 : Xiuqin Deng,Jiadi Deng (검색결과 2 건)
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The Naive Bayesian Algorithm-based Prisoner’s Dilemma Game Model
Xiuqin Deng,Jiadi Deng 보안공학연구지원센터 2014 International Journal of u- and e- Service, Scienc Vol.7 No.6
Prisoners’ dilemma is a typical game theory issue. In this study, it was treated as an incomplete information game to establish a related machine learning model using a naive Bayesian classification method. The model established was referred to as the Bayes model. Using this model, the incomplete information game was soluble with the assistance of statistical machine learning. This study proceeded as follows: firstly, four typical models were run against the Bayes model some 10,000 times. The total incomes of the models recorded suggested that Bayes model was more advantageous than other models. Even in a multi-player prisoners’ game, Bayes model also presented the desired level of performance and accrued a higher income than other models. Further statistical analysis implied that the Bayes model and the widely accepted optimum strategy tit-for-tat (TFT) model showed a tendency to be prone to defection. Secondly, according to the games run on the natural Bayes model, as well as the natural TFT model, it was found that the Bayes model accrued more benefits than the TFT model on average. Finally, comparison of the Bayes model with the TFT model revealed that the Bayes model was better. This demonstrated the efficacy of the Bayes model constructed in this study and moreover, provided a novel idea for solving the problem of an incomplete information game.
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The Research and Implementation of Temporal Quasi-order Data Index
Xiuqin Deng,Hongyan Xing,Chengyan Fang,Xiaoping Ye 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.1
This study proposed a quasi-order-based temporal data structure (QOTDS) which differed from conventional, algebraic data management models. Based on this QOTDS, a temporal data index called the temporal quasi-order index (TQOindex) was established. Firstly, the study proposed the concepts of temporal quasi-order (TQO) and linear order partitioning (LOP) of time period sets and discussed the construction algorithm of LOP and the optimum (minimum) properties. On this basis, a temporal data structure was established based on LOP. This structure realized the set-at-a-time data operation-like relational data structure and improved the inquiry efficiency by using multiple threads. Subsequently, in the structural framework of TQO, we discussed the temporal data index (TQOindex) based on quasi-order extensions. This index was effectively applicable to various conventional database platforms depending on the disk (external memory)-based data management and also to big data dynamic index technology relying on the incremental updating mechanism. Finally, a corresponding experimental simulation and comparative evaluation were designed to verify the feasibility and effectiveness of TQOindex. Research and experiments showed that QOTDS were effective at temporal inquiry and management in cases involving the temporal processing and integration mechanisms in new data, such as semantic data, XML data, and moving object data.
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