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

Quteishat, Anas, Lim, Chee Peng, Saleh, Junita Mohamad, Tweedale, Jeffrey and Jain, Lakhmi C. 2011, A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement, Soft computing, vol. 15, no. 2, pp. 221-231.

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Title A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement
Author(s) Quteishat, Anas
Lim, Chee Peng
Saleh, Junita Mohamad
Tweedale, Jeffrey
Jain, Lakhmi C.
Journal name Soft computing
Volume number 15
Issue number 2
Start page 221
End page 231
Total pages 11
Publisher Springer
Place of publication Heidelberg, Germany
Publication date 2011-02
ISSN 1432-7643
1433-7479
Keyword(s) Bayesian belief function
fuzzy min-max neural network
multi-agent classifier systems
neural networks
trust measurement
Summary 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.
Language eng
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2010, Springer-Verlag
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048106

Document type: Journal Article
Collection: Institute for Frontier Materials
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