A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement

被引：0
作者：
Anas Quteishat
Chee Peng Lim
Junita Mohamad Saleh
Jeffrey Tweedale
Lakhmi C. Jain
机构：
[1] Al-Balqa’ Applied University,Department of Computer Engineering, Faculty of Engineering Technology
[2] University of Science Malaysia,School of Electrical and Electronic Engineering
[3] University of South Australia,School of Electrical and Information Engineering
来源：
Soft Computing | 2011年 / 15卷
关键词：
Multi-agent classifier systems; Neural networks; Trust measurement; Bayesian belief function; Fuzzy Min-Max neural network;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication (TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions, is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated, with the implications analysed and discussed.
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页码：221 / 231
页数：10
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