A neural network-based multi-agent classifier system

Quteishat, Anas; Lim, Chee Peng; Tweedale, Jeffrey W.; Jain, Lakhmi C.

doi:10.1016/j.neucom.2008.08.012

Cited by 46 publications

(14 citation statements)

References 25 publications

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“…DSS assesses the impact of personnel efficiency by data envelopment analysis (DEA), artificial neural network (ANN), rough set theory (RST), and K-Means clustering algorithm. Quteishat et al (2009) 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.…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

“…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 (Quteishat et al 2009). Tran et al (2004) suggests a decision support system for tactical air combat environment where not much prior information is available about the decision regions ).…”

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

“…This is evidenced by the widespread application of multi-agent systems to different domains including e-commerce, healthcare, military support, decision support, knowledge management, as well as control systems (Quteishat et al 2009). …”

Section: Intelligent Agent-assisted Decision Support Systemsmentioning

confidence: 99%

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Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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This chapter presents the latest IDSSs, such as text analytics and mining based DSSs; ambient intelligence and the internet of things-based DSSs; biometrics-based DSSs; recommender, advisory and expert systems; data mining, data analytics, neural networks, remote sensing and their integration with decision support systems and other IDSSs. These other IDSSs include GA-based DSS; fuzzy sets DSS; rough sets-based DSS; intelligent agent-assisted DSS; process mining integration to decision support, adaptive DSS; computer vision based DSS; sensory DSS and robotic DSS. In addition to acquainting these IDSSs, author introduce practical examples where they have been effectively applied. 2

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Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

Section: Artificial Neural Network In Decision Support Systems and Bmentioning

confidence: 99%

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Intelligent Decision Support Systems

Kaklauskas

2014

Intelligent Systems Reference Library

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“…al [18] used a duplicated ANN at each node to train a subset of patterns from the training set in parallel. In [19], A. Quteishat et al presented an ANN based on multi-agent classifier system. These agents work in parallel and form two interacted agent teams where the communication strategy followed the TNC (trust, negociation, communication) reasoning model.…”

Section: Related Work Of Ann' Parallel Trainingmentioning

confidence: 99%

Multi-agent architecture for Multi‐objective optimization of Flexible Neural Tree

et al. 2016

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“…It has the capacity to deal with heterogeneous, distributed, large and complex applications and environments in different area such as optimization, neural network [5], [40], robotics [27], fuzzy system [25], etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent evolutionary design of Flexible Beta Basis Function Neural Tree

Ammar

Bouaziz

Alimi

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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Multi-Agent System (MAS) is a very active field that ensures global coherence between agents'interactions in a distributed way and implicit global control. Under the awareness of its power, the application of MAS was no more limited to very specific problems, but to almost application area: optimization, neural network, robotics, fuzzy system, etc. In the other side, a complex system of Artificial Neural Network called Flexible Beta Basis Function Neural Tree (FBBFNT) has reached a great level in the prediction search domain. In the purpose of enlarging the application of the algorithm to complex applications of the real problems, a new architecture of MAS was designed and applied to the FBBFNT process. This new multi-agent system based on communications and negotiations allowed the resolution of more complex prediction problems and the acceleration of the global convergence speed.

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A neural network-based multi-agent classifier system

Cited by 46 publications

References 25 publications

Intelligent Decision Support Systems

Intelligent Decision Support Systems

Multi-agent architecture for Multi‐objective optimization of Flexible Neural Tree

Multi-agent evolutionary design of Flexible Beta Basis Function Neural Tree

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