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

journal contribution
posted on 2011-02-01, 00:00 authored by A Quteishat, Chee Peng Lim, J Saleh, J Tweedale, L Jain
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.

History

Journal

Soft computing

Volume

15

Issue

2

Pagination

221 - 231

Publisher

Springer

Location

Heidelberg, Germany

ISSN

1432-7643

eISSN

1433-7479

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2010, Springer-Verlag