Abstract
The need of better representation of complex systems, such social systems, has made that the use of new simulation techniques are increasingly accepted, one of these accepted techniques are multi-agent systems. In addition to represent the uncertainty that is required by them, fuzzy logic and particularly type-2 fuzzy logic are being accepted. A system with three different types of agents is presented as case of study, each agent is assigned to a role with specific goals to be achieved in both ways individually and as teams, the success or failure is determined by group performance rather than individual achievement. It is also taken into account the environment or context as another type of agent. Fuzzy inference systems are defined for each of the agents to represent the concepts interpretation.
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References
Yolles, M.: Organizations as Complex Systems: An Introduction to Knowledge Cybernetics. Information Age Publishing, Greenwich (2006)
Brownlee, J.: Complex Adaptive Systems Technical Report 070302A (2007)
Miler, J., Page, S.: Complex Adaptive Systems: An Introduction to Computational Models of Social Life, p. 284. Princeton University Press (2007)
Long, L.N., Hanford, S.D., Janrathitikarn, O., Sinsley, G.L., Miller, J.A.: A Review of Intelligent Systems Software for Autonomous Vehicles. In: Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications (2007)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning, iii. Information Science 8(199249), 301–357 (1975)
Jang, J.S., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. MATLAB Curriculum Series. Prentice Hall, Upper Saddle River (1997)
Mendel, J.M.: The Perceptual Computer: An Architecture for Computing with Words. In: Proc. IEEE Int. Conf. Fuzzy Syst. (FUZZ-IEEE 2001), Melbourne, Australia, pp. 35–38 (2001)
Mendel, J.M.: An Architecture for Making Judgments using Computing with Words. Int. J. Appl. Math. Comput. Sci. 12(3), 325–335 (2002)
Mendel, J.M.: Computing with Words and its Relationships with Fuzzistics. Inf. Sci. 177, 988–1006 (2007)
Mendel, J.M., Wu, D.R.: Perceptual Reasoning for Perceptual Computing. IEEE Transactions on Fuzzy Systems 16(6), 1550–1564 (2008)
Turksen, I.B.: Type-2 Representation and Reasoning for CWW. Fuzzy Sets Syst. 127, 17–36 (2002)
Wagner, C., Hagras, H.: Fuzzy Composite Concepts based on Human Reasoning. In: IEEE International Conference on Information Reuse and Integration (IRI 2010), Las Vegas, Nevada, USA, p. 308 (2010), doi:978-1-4244-8099-9
Leal-Ramírez, C., Castillo, O., Melin, P., Rodríguez-Díaz: Simulation of the bird age-structured population growth based on an interval type-2 fuzzy cellular structure. Inf. Sci. 181(3), 519–535 (2011)
Castillo, O., Melin, P., Alanis-Garza, A., Montiel, O., Sepúlveda, R.: Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms. Soft Computing 15(6), 1145–1160 (2011)
Melin, P., Mendoza, O., Castillo, O.: An improved method for edge detection based on interval type-2 fuzzy logic. Expert Systems Applications 37(12), 8527–8535 (2010)
Castro, J.R., Castillo, O., Melin, P., Rodríguez-Díaz: A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks. Inf. Sci. 179(13), 2175–2193 (2009)
Castillo, O., Aguilar, L.T., Cázarez-Castro, N.R., Cardenas, S.: Systematic design of a stable type-2 fuzzy logic controller. Applications Soft Computing 8(3), 1274–1279 (2008)
Sepúlveda, R., Castillo, O., Melin, P., Montiel, O.: An Efficient Computational Method to Implement Type-2 Fuzzy Logic in Control Applications. Analysis and Design of Intelligent Systems using Soft Computing Techniques, 45–52 (2007)
Orchard., R.: Fuzzy Reasoning in Jess: The Fuzzy J Toolkit and Fuzzy Jess. In: Proceedings of the Third International Conference on Enterprise Information Systems, ICEIS, pp. 533–542 (2001)
Jammeh, E., Fleury, M., Wagner, C., Hagras, H., Ghanbari, M.: Interval Type-2 Fuzzy Logic Congestion Control for Video Streaming across IP Networks. IEEE Transaction on Fuzzy Systems 17(5), 1123–1142 (2009)
Lee, C., Wang, M., Hagras, H.: A Type-2 Fuzzy Ontology and its Application to Personal Diabetic Diet Recommendation. IEEE Transactions on Fuzzy Systems 18(2), 374–395 (2010)
Gaxiola-Pacheco, C., Flores, D.L., Castañón-Puga, M., Rodríguez-Díaz, A., Castro, J.R., Espinoza-Hernández, I.: Extending Jess with Type-2 Fuzzy Logic. In: Advances in Soft Computing Science. Research in Computing Science, vol. (49), pp. 121–129 (2010) ISSN 1870-4069
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Flores, DL., Castañón-Puga, M., Gaxiola-Pacheco, C. (2011). A Complex Social System Simulation Using Type-2 Fuzzy Logic and Multiagent System. In: Batyrshin, I., Sidorov, G. (eds) Advances in Artificial Intelligence. MICAI 2011. Lecture Notes in Computer Science(), vol 7094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25324-9_6
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DOI: https://doi.org/10.1007/978-3-642-25324-9_6
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