A neural network-based multi-agent classifier system

Authors:
Anas Quteishat;Chee Peng Lim;Jeffrey Tweedale;Lakhmi C. Jain
Affiliations:
School of Electrical and Electronic Engineering, University of Science Malaysia, Malaysia;School of Electrical and Electronic Engineering, University of Science Malaysia, Malaysia;School of Electrical and Information Engineering, University of South Australia, Australia and Defence Science and Technology Organisation, Edinburgh, South Australia, Australia;School of Electrical and Information Engineering, University of South Australia, Australia
Venue:
Neurocomputing
Year:
2009

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Abstract

In this paper, we propose a neural network (NN)-based multi-agent classifier system (MACS) using the trust, negotiation, and communication (TNC) reasoning model. The main contribution of this work is that a novel trust measurement method, based on the recognition and rejection rates, is proposed. Besides, an auctioning procedure, based on the sealed bid, first price method, is adapted for the negotiation phase. Two agent teams are formed; each consists of three NN learning agents. The first is a fuzzy min-max (FMM) NN agent team and the second is a fuzzy ARTMAP (FAM) NN agent team. Modifications to the FMM and FAM models are also proposed so that they can be used for trust measurement in the TNC model. To assess the effectiveness of the proposed model and the bond (based on trust), five benchmark data sets are tested. The results compare favorably with those from a number of classification methods published in the literature.