File(s) under permanent embargo
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 LimChee Peng Lim, J Saleh, J Tweedale, L JainIn 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 computingVolume
15Issue
2Pagination
221 - 231Publisher
SpringerLocation
Heidelberg, GermanyISSN
1432-7643eISSN
1433-7479Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2010, Springer-VerlagUsage metrics
Keywords
Bayesian belief functionfuzzy min-max neural networkmulti-agent classifier systemsneural networkstrust measurementScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Interdisciplinary ApplicationsComputer ScienceCAPACITANCETOMOGRAPHYDESIGNMODELSArtificial Intelligence and Image Processing
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC