Abstract
In this paper, we propose a Multi-Agent Classifier (MAC) system based on the Trust, Negotiation, and Communication (TNC) model. A novel trust measurement method, based on the recognition and rejection rates, is proposed. Two agent teams, each consists of three neural network (NN) agents, are formed. The first is the Fuzzy Min-Max (FMM) NN agent team and the second is the Fuzzy ARTMAP (FAM) NN agent team. An auctioning method is also used for the negotiation phase. The effectiveness of the proposed model and the bond (based on trust) is measured using two benchmark classification problems. The bootstrap method is applied to quantify the classification accuracy rates statistically. The results demonstrate that the MAC system is able to improve the performances of individual agents as well as the team agents. The results also compare favorably with those from other methods published in the literature.
Highlights
The Multi-Agent System (MAS) approach has gained much research interest over the last decade
The proposed Multi-Agent Classifier (MAC) system consists of an ensemble of neural network (NN)-based classifiers
The results are compared with those from a number of machine learning systems published in the literature
Summary
The Multi-Agent System (MAS) approach has gained much research interest over the last decade. A number of models have been used to describe the relation between agents in MASs, and one of the earliest models is the Beliefs, Desires, Intentions (BDI) reasoning model [7]. Another model used in MAS is the decision support pyramid model [8]. A novel method to measure trust by using the classification accuracy rates of the agent is proposed.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
