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On the Implementation of Evolving Dynamic Cognitive Maps

  • Joao Paulo CarvalhoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1000)

Abstract

Fuzzy Cognitive Maps (FCM) and other Dynamic Cognitive Maps (DCM) allow simulation of the evolution of complex qualitative dynamic systems through time. However, the DCM model is static by itself in the sense that its cognitive configuration, i.e., the concepts’ definitions, the relations among the concepts and the structure of the map, do not change with time. This paper introduces DCM meta-states, a simple but versatile Finite State Machine based mechanism that can be used to implement Evolving FCM and generic Evolving Dynamic Cognitive Maps (Ev-DCM).

Keywords

Fuzzy Cognitive Maps Rule-Based Fuzzy Cognitive Maps Dynamic Cognitive Maps Evolving temporal modeling 

References

  1. 1.
    Acampora, G., Loia, V.: On the temporal granularity in fuzzy cognitive maps. IEEE Trans. Fuzzy Syst. 9(6), 1040–1057 (2011)CrossRefGoogle Scholar
  2. 2.
    Alur, R.: A theory of timed automata. Theor. Comput. Sci. 126, 183–235 (1994)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Axelrod, R.: The Structure of Decision: Cognitive Maps of Political Elites. Princeton University Press, Princeton (1976)Google Scholar
  4. 4.
    Carvalho, J.P., Tomé, J.A.B.: Rule based fuzzy cognitive maps – fuzzy causal relations. In: Mohammadian, M. (ed.) Computational Intelligence for Modelling, Control and Automation: Evolutionary Computation & Fuzzy Logic for Intelligent Control, Knowledge Acquisition & Information Retrieval. IOS Press, Amsterdam (1999)Google Scholar
  5. 5.
    Carvalho, J.P., Carola, M., Tome, J.A.: Forest fire modelling using rule-based fuzzy cognitive maps and Voronoi based cellular automata. In: Proceedings of the 25th International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2006, Montreal, Canada (2006)Google Scholar
  6. 6.
    Carvalho, J.P., Carola, M., Tomé, J.A.: Using rule based fuzzy cognitive maps to model dynamic cell behaviour in Voronoi based cellular automata. In: Proceedings of the WCC I2006 – 2006 IEEE World Congress on Computational Intelligence, pp. 1503–1510 (2006)Google Scholar
  7. 7.
    Carvalho, J.P.: On the semantics and the use of fuzzy cognitive maps in social sciences. In: Proceedings of the WCCI 2010 – 2010 IEEE World Congress on Computational Intelligence, Barcelona, pp. 2456–2461 (2010)Google Scholar
  8. 8.
    Carvalho, J.P.: On the semantics and the use of fuzzy cognitive maps and dynamic cognitive maps in social sciences. Fuzzy Sets Syst. 214, 6–19 (2013)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Carvalho, J.P., Tomé, J.A.: Fuzzy mechanisms for qualitative causal relations. In: Seising, R. (ed.) Views on Fuzzy Sets and Systems from Different Perspectives. Philosophy and Logic, Criticisms and Applications. Studies in Fuzziness and Soft Computing. Springer, Berlin (2009). Chapter 19Google Scholar
  10. 10.
    Carvalho, J.P., Tomé, J.A.: Rule based fuzzy cognitive maps in socio-economic systems. In: Proceedings of the IFSA-EUSFLAT 2009 - International Fuzzy systems Association World Congress, European Society for Fuzzy Logic and Technology International Conference, pp. 1821–1826 (2009)Google Scholar
  11. 11.
    Carvalho, J.P., Tomé, J.A.: Rule based fuzzy cognitive maps - expressing time in qualitative system dynamics. In: Proceedings of the 2001 FUZZ-IEEE Conference, Melbourne, Australia (2001)Google Scholar
  12. 12.
    Carvalho, J.P., Tomé, J.A.: Rule based fuzzy cognitive maps – qualitative systems dynamics. In: Proceedings of the 19th International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2000, Atlanta, pp. 407–411 (2000)Google Scholar
  13. 13.
    Carvalho, J.P., Wise, L., Murta, A., Mesquita, M.: Issues on dynamic cognitive map modelling of purse-seine fishing skippers behavior. In: Proceedings of the WCCI 2008 – 2008 IEEE World Congress on Computational Intelligence, Hong-Kong, pp. 1503–1510 (2008)Google Scholar
  14. 14.
    Hagiwara, M.: Extended fuzzy cognitive maps. In: Proceedings of IEEE International Conference on Fuzzy Systems, pp. 795–801 (1992)Google Scholar
  15. 15.
    Kosko, B.: Fuzzy cognitive maps. Int. J. Man-Mach. Stud 24(1), 65–75 (1986)CrossRefGoogle Scholar
  16. 16.
    Kosko, B.: Fuzzy Thinking. Hyperion, Santa Clara (1993)zbMATHGoogle Scholar
  17. 17.
    Kosko, B.: Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice-Hall International Editions, Upper Saddle River (1992)zbMATHGoogle Scholar
  18. 18.
    Kottas, T.L., Boutalis, Y.S., Christodoulou, M.A.: Fuzzy cognitive network: a general framework. Intell. Decis. Technol 1, 183–196 (2007)CrossRefGoogle Scholar
  19. 19.
    Laukkanen, M.: Conducting causal mapping research: opportunities and challenges. In: Eden, C., Spender, J.-C. (eds.) Managerial and Organisational Cognition. Sage, Thousand Oaks (1998)Google Scholar
  20. 20.
    Miao, Y., Liu, Z., Siew, C., Miao, C.: Dynamical cognitive network - an extension of fuzzy cognitive map. IEEE Trans. Fuzzy Syst. 9(5), 760–770 (2001)CrossRefGoogle Scholar
  21. 21.
    Minsky, M.: Computation: Finite and Infinite Machines, 1st edn. Prentice-Hall, Upper Saddle River (1967)zbMATHGoogle Scholar
  22. 22.
    Sipser, M.: Introduction to the Theory of Computation, Second Edition, International Edition, Thomson Course Technology (2006)Google Scholar
  23. 23.
    Wise, L., Murta, A., Carvalho, J.P., Mesquita, M.: Qualitative modelling of fishermen’s behaviour in a pelagic fishery. Ecol. Model. 228, 112–122 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.INESC-ID/Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal

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