Abstract
A novel trust measurement method, namely, certified belief in strength (CBS), for a multi-agent classifier system (MACS) is proposed in this paper. The CBS method aims to improve the performance of the constituent agents of the MACS, viz., the fuzzy min–max (FMM) neural network classifier. Trust measurement is accomplished using reputation and strength of the constituent agents. Trust is built from strong elements that are associated with the FMM agents, allowing the CBS method to improve the performance of the MACS. An auction procedure based on the sealed bid, namely, the first price method, is adopted for the MACS in determining the winning agent. The effectiveness of the CBS method and the bond (based on trust) is verified by using a number of benchmark data sets. The results demonstrate that the proposed MACS-CBS model is able to produce better accuracy and stability as compared with those from other existing methods.
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Shemshadi A, Soroor J, Tarokh MJ (2008) An innovative framework for the new generation of SCORM 2004 conformant e-learning systems. In: International conference on information technology: new generations, pp 949–954
Mcarthur SDJ, Davidson EM, Catterson VM, Dimeas AL, Hatziargyriou ND, Ponci F, Funabashi T (2007) Multi-agent systems for power engineering applications-part I: concepts, approaches, and technical challenges. IEEE Trans Power Syst 22(4):1743–1752
Shen X, Shirmohammadi S, Desmarais S, Georganas ND (2006) Multi-agent system architecture for collaborative E-commerce. In: Canadian conference on electrical and computer engineering-CCECE, pp 284–287
Davidson EM, Mcarthur SDJ, Mcdonald JR, Cumming T, Watt I (2006) Applying multi-agent system technology in practice: automated management and analysis of SCADA and digital fault recorder data. IEEE Trans Power Syst 21(2):559–567
Koesrindartoto D, Junjie S, Tesfatsion L (2005) An agent-based computational laboratory for testing the economic reliability of wholesale power market designs. IEEE Conf Power Eng Soc Gen Meet 3:2818–2823
Widergren SE, ROOP JM, GUTTROMSON RT, HUANG Z (2004) Simulating the dynamic coupling of market and physical system operations. IEEE Power Eng Soc Gen Meet 1:748–753
Han BM, Song SJ, Lee KM, Jang KS, Shin DR (2006) Multi-agent system based efficient healthcare service. Int Conf Adv Commun Technol 1:5–51
Manojlovich J, Prasithsangaree P, Hughes S, Chen J, Lewis M (2003) UTSAF: a multi-agent-based framework for supporting military-based distributed interactive simulations in 3D virtual environments. Simul Conf 1:960–968
Wu F, Zilberstein S, Chen X (2011) Online planning for multi-agent systems with bounded communication. Artif Intell 175(2):487–511
Hoang A (1997) Supervised classifier performance on the UCI DataBase. Department of Computer Science, University of Adelaide, M.Sc. Thesis
Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 30(4):451–462
Gabrys B, Bargiela A (2000) General fuzzy min-max neural network for clustering and classification. IEEE Trans Neural Networks 11(3):769–783
Nandedkar AV, Biswas PK (2007) A fuzzy min-max neural network classifier with compensatory neuron architecture. IEEE Trans Neural Networks 18(1):42–54
Quteishat A, Lim CP (2008) A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification. Appl Soft Comput 8(2):985–995
Quteishat A, Lim CP, Saleh JM, Tweedale J, Jain LC (2011) A neural network-based Multi-agent Classifier System with a Bayesian formalism for trust measurement. Soft Comput 15(2):221–231
Quteishat A, Lim CP, Tweedale J, Jain LC (2009) A neural network-based Multi-agent Classifier System. Neurocomputing 72(7–9):1639–1647
Simpson PK (1992) Fuzzy min-max neural networks. I. Classification. IEEE Trans Neural Networks 3(5):776–786
Simpson PK (1993) Fuzzy min-max neural networks—Part 2: clustering. IEEE Trans Fuzzy Syst 1(1):32
Bentahar J, Khosravifar B (2008) Using trustworthy and referee agents to secure multi-agent systems. In: The fifth international conference on information technology: new generations-ITNG, pp 477–482
Khosravifar B, Gomrokchi M, Bentahar J, Thiran P (2009) Maintenance-based trust for multi-agent systems. Int Conf Auton Agents Multiagent Syst 2:1017–1024
Yolum P, Singh MP (2005) Engineering self-organizing referral networks for trustworthy service selection. IEEE Trans Syst Man Cybern Part A Syst Hum 35(3):396–407
Ries S, Kangasharju J, Mühlhäuser M (2006) A Classification of Trust Systems. Lect Notes Comput Sci Move Meaningful Intern Syst 4277:894–903
Dong F, Huang L, Yang W, Zhu Y, Wang J (2010) A general model for trust and reputation systems. IEEE Int Conf Comput Sci Inf Technol 4:454–459
Huynh TD, Jennings NR, Shadbolt NR (2006) An integrated trust and reputation model for open multi-agent systems. Auton Agent Multi-Agent Syst 13(2):119–154
Sabater J, Sierra C (2005) Review on computational trust and reputation models. Artif Intell Rev 24(1):33–60
Mui L, Mohtashemi M, Halberstadt A (2002) A computational model of trust and reputation. In: Hawaii international conference on system sciences-HICSS, pp 2431–2439
Boukerche A, Li X (2005) An agent-based trust and reputation management scheme for wireless sensor networks. IEEE Glob Telecommun Conf 3:5
Wang P, Zhang Z (2005) A computation trust model with trust network in multi-agent systems. In: International conference on active media technology-AMT, pp 389–392
Zheng X, Wu Z, Chen H, Mao Y (2006) Developing a composite trust model for multi-agent systems. In: International joint conference on autonomous agents and multi-agent systems-AAMAS, pp 1257–1259
Li B, Junwu M, Zhu J, Che T (2008) A dynamic trust model for the multi-agent systems. In: International symposiums on information processing-ISIP, pp 500–504
Newman DJ, Asuncion A, Hettich S et al. (2011) UCI repository of machine learning databases [Online]. [Accessed, available at http://archive.ics.uci.edu/ml/, Last visit [Online]. Accessed 14 Jun 2011
Benaim M, Samuelides M (1991) A rigorous result about the off-line learning approximation. Int Jt Conf Neural Netw 2:979
Nakashima T, Uenishi T, Narimoto Y (2010) Off-line learning of soccer formations from game logs. In: IEEE conference on world automation congress-WAC, pp 1–6
Odeh SM, Khalil M (2011) Off-line signature verification and recognition: neural network approach. In: International symposium on innovations in intelligent systems and applications-INISTA, pp 34–38
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co., Inc., Boston
Ishibuchi H, Nakashima T, Murata T (1999) Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans Syst Man Cybern B Cybern 29(5):601–618
Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7(1):1–26
Holte RC (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn 11(1):63–90
Elomaa T, Rousu J (1999) General and efficient multisplitting of numerical attributes. Mach Learn 36(3):201–244
Ishibuchi H, Yamamoto T, Nakashima T (2005) Hybridization of fuzzy GBML approaches for pattern classification problems. IEEE Trans Syst Man Cybern B Cybern 35(2):359–365
Yeung DS, Ng WWY, Wang D, Tsang ECC, Wang XZ (2007) Localized generalization error model and its application to architecture selection for radial basis function neural network. IEEE Trans Neural Networks 18(5):1294–1305
Corcoran AL, Sen S (1994) Using real-valued genetic algorithms to evolve rule sets for classification. IEEE Int Conf Evol Comput 1:120–124
Acknowledgments
The authors gratefully acknowledge the partial financial support of the FRGS grants (No. 6711229 and 6711195) for this work.
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Mohammed, M.F., Lim, C.P. & Quteishat, A. A novel trust measurement method based on certified belief in strength for a multi-agent classifier system. Neural Comput & Applic 24, 421–429 (2014). https://doi.org/10.1007/s00521-012-1245-2
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DOI: https://doi.org/10.1007/s00521-012-1245-2