Abstract
In this paper, we propose a Multi-Agent Classifier (MAC) system based on the Trust-Negotiation-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) agent team and the second is the Fuzzy ARTMAP (FAM) 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 proposed 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.
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References
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, Department of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Balogh, Z., Laclavik, M., Hluchy, L.: Multi Agent System for Negotiation and Decision Support. In: Proceeding of fourth International Scientific Conference Electronic Computers and Informatics, Košice - Herľany, Slovakia, pp. 264–270 (2000)
Beer, M., D’inverno, M., Jennings, N., Luck, M., Preist, C., Schroeder, M.: Negotiation in Multi-Agent Systems. Knowledge Engineering Review 14, 285–289 (1999)
Bratman, M.E.: Intention, Plans, and Practical Reason. University of Chicago Press (1999)
Carpenter, G., Tan, A.: Rule extraction: From neural architecture to symbolic representation. Connection Science 7, 3–27 (1995)
Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J., Rosen, D.B.: Fuzzy ARTMAP: A neural network architecture for incremental learning of analog multidimensional maps. IEEE Trans. Neural Networks 3, 698–713 (1992)
Egmont-Petersen, M., Talmon, J.L., Brender, J., Ncnair, P.: On the quality of neural net classifiers. Artificial Intelligence in Medicine 6, 359–381 (1994)
Gwebu, K., Wang, J., Troutt, M.D.: Constructing a Multi-Agent System: An Architecture for a Virtual Marketplace. In: Phillips-Wren, G., Jain, L. (eds.) Intelligent Decision Support Systems in Agent-Mediated Environments. IOS Press, Amsterdam (2005)
Haider, K., Tweedale, J., Urlings, P., Jain, L.: Intelligent Decision Support System in Defense Maintenance Methodologies. In: International Conference on Emerging Technologies ICET 2006, pp. 560–567 (2006)
Hoang, A.: Supervised Classifier Performance on the UCI Data Set. Department of Computer Science. Australia, University of Adelaide (1997)
Hudson, D.L., Cohen, M.E.: Use of intelligent agents in the diagnosis of cardiac disorders. In: Computers in Cardiology, pp. 633–636 (2002)
Kelly, C., Boardman, M., Goillau, P., Jeannot, E.: Guidelines for trust in future ATM systems: A literature review. Technical Report 030317-01, European Organization for Safety of Air Navigation (2003)
Ossowski, S., Fernandez, A., Serrano, J.M., Hernandez, J.Z., Garcia-Serrano, A.M., Perez-De-La-Cruz, J.L., Belmonte, M.V., Maseda, J.M.: Designing multiagent decision support system the case of transportation management. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems AAMA, pp. 1470–1471 (2004)
Ossowski, S., Hernandez, J.Z., Iglesias, C.A., Ferndndez, A.: Engineering agent systems for decision support. In: Third International Workshop Engineering Societies in the Agents World ESAW 2002, Madrid, Spain, pp. 184–198 (2002)
Simpson, P.K.: Fuzzy Min-Max neural networks-Part 1: Classification. IEEE Transactions on Neural Networks 3, 776–786 (1992)
Singh, R., Salam, A., Lyer, L.: Using agents and XML for Knowledge representation and exchange: An intelligent distributed decision support architecture. In: Proceeding of the Ninth American Conference on Information Systems, pp. 1853–1864 (2003)
Tolk, A.: An Agent-Based Decision Support System Architecture for the Military Domain. In: Phillips-Wren, G., Jain, L. (eds.) Intelligent Decision Support Systems in Agent-Mediated Environments, ISO Press (2005)
Tweedale, J., Cutler, P.: Trust in Multi-Agent Systems. In: Proceeding of the 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Bournemouth UK, pp. 479–485. Springer, Heidelberg (2006)
Vahidov, R., Fazlollahi, B.: Pluralistic multi-agent decision support system: a framework and an empirical test. Information and Management 41, 883–898 (2004)
Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans Systems, Man, and Cybernetics 22, 418–435 (1992)
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Quteishat, A., Lim, C.P., Tweedale, J., Jain, L.C. (2009). A Multi-Agent Classifier System Based on the Trust-Negotiation-Communication Model. In: Avineri, E., Köppen, M., Dahal, K., Sunitiyoso, Y., Roy, R. (eds) Applications of Soft Computing. Advances in Soft Computing, vol 52. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88079-0_10
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DOI: https://doi.org/10.1007/978-3-540-88079-0_10
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