Abstract
Negotiation is one of the most prevalent methods that agents, in a multi-agent system, use to reach agreements. Nowadays, one important aspect of negotiation is moral behaviors of agents that involve in negotiation. For this reason, we propose an ethical classifier that uses artificial neural networks ensembles. To evaluate the performance of the proposed method, we conduct experiments including comparisons with alternative methods for ethical classification. As the result of experiments suggest, the proposed method shows improved ethical recognition performance, in comparison with other widely used methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Wiegel, V., van den Berg, J.: Combining Moral Theory, Modal Logic and Mas to Create Well-Behaving Artificial Agents. Int. J. Soc. Robot. 1, 233–242 (2009)
Choi, S.P.M., Liu, J., Chan, S.P.: A genetic agent-based negotiation system. Computer Networks 37(2), 195–204 (2001)
Oprea, M.: An adaptive negotiation model for agent-based electronic commerce. Studies in Informatics and Control 11(3), 271–279 (2002)
Wiegel, V., van den Berg, J.: Experimental Computational Philosophy: shedding new lights on old philosophical debates, pp. 62–67 (2008)
Liu, N., Zheng, D.X., Xiong, Y.H.: Multi-agent negotiation model based on rbf neural network learning mechanism, pp. 133–136. IEEE (2008)
Sozen, A., Arcaklioglu, E.: Exergy analysis of an ejector-absorption heat transformer using artificial neural network approach. Applied Thermal Engineering 27(2-3), 481–491 (2007)
Nissen, S.: Implementation of a fast artificial neural network library (fann). Report, Department of Computer Science University of Copenhagen (DIKU) 31 (2003)
Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)
Nigrin, A.: Neural networks for pattern recognition. The MIT press (1993)
Tumer, K., Ghosh, J.: Error correlation and error reduction in ensemble classifiers. Connection Science 8(3-4), 385–404 (1996)
Liu, Y., Yao, X., Higuchi, T.: Evolutionary ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation 4(4), 380–387 (2000)
Perrone, M.P.: When networks disagree: Ensemble methods for hybrid neural networks. In. DTIC Document (1992)
Sharkey, A.J.: Combining artificial neural nets: ensemble and modular multi-net systems. Springer-Verlag New York, Inc. (1999)
Shimshoni, Y., Intrator, N.: Classification of seismic signals by integrating ensembles of neural networks. IEEE Transactions on Signal Processing 46(5), 1194–1201 (1998)
Rosen, B.E.: Ensemble learning using decorrelated neural networks. Connection Science 8(3-4), 373–384 (1996)
Weitz, B.A., Castleberry, S.B., Tanner, J.F., Irwin/McGraw-Hill, Companies, M.-H., Achieve Global, I.: Selling: building partnerships (2004)
Honarvar, A.R., Ghasem-Aghaee, N.: An artificial neural network approach for creating an ethical artificial agent, pp. 290–295. IEEE Press (2009)
Al-Fedaghi, S.S.: Typification-based ethics for artificial agents, pp. 482–491. IEEE (2008)
Floridi, L., Sanders, J.W.: On the morality of artificial agents. Minds and Machines 14(3), 349–379 (2004)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–18 (2009)
Cohen, S.: Negotiating skills for managers. McGraw-Hill Companies (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rekabdar, B., Joorabian, M., Shadgar, B. (2012). Artificial Neural Network Ensemble Approach for Creating a Negotiation Model with Ethical Artificial Agents. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-29350-4_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29349-8
Online ISBN: 978-3-642-29350-4
eBook Packages: Computer ScienceComputer Science (R0)