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Using RuleML for Representing and Prolog for Simulating Fuzzy Cognitive Maps

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Fuzzy Cognitive Maps for Applied Sciences and Engineering

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 54))

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Abstract

Fuzzy Cognitive Map (FCM) technique is broadly used for decision making and predictions by experts and scientists of a wide range of disciplines. The use of the FCMs would be even wider if a standardized representation of FCMs was developed and a system that would simulate them was constructed. Having such a system, decision makers would be able to create and examine their own developed Fuzzy Cognitive Maps, and also distribute them e.g. through Internet. In this chapter, (a) we propose a RuleML representation of FCMs and (b) we present the design and implementation of a system that assists experts to simulate their own FCMs. This system, which is developed using the Prolog programming language, makes the results of the FCM simulation directly available to other cooperative systems because it returns them in standard RuleML syntax. In the chapter, the design choices of the implemented system are discussed and the capabilities of the RuleML representation of FCM are presented. The use of the system is exhibited by a number of examples concerning an e-business company.

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Notes

  1. 1.

    http://ruleml.org/1.0/

References

  1. Boley, H.: The RuleML Family of Web Rule Languages. In: Alferes, J.J., Bailey, J., May, W., Schwertel, U. (eds.) PPSWR 2006. LNCS, vol. 4187, pp. 1–17, Springer, Heidelberg (2006)

    Google Scholar 

  2. Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24, 65–75 (1986)

    Article  MATH  Google Scholar 

  3. Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall, Englewood Cliffs (1992)

    Google Scholar 

  4. Axelrod, R.: Structure of Decision. Princeton University Press, Princeton (1976)

    Google Scholar 

  5. Khan, M.S., Chong, A., Gedeon, T.: A methodology for developing adaptive fuzzy cognitive maps for decision support. J. Adv. Comput. Intell. 4(6), 403–407 (2000)

    Google Scholar 

  6. Khan, M.S., Quaddus, M.: Group decision support using fuzzy cognitive maps for causal reasoning. Group Decis. Negot. 13, 463–480 (2004)

    Article  Google Scholar 

  7. Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 153(3), 371–401 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  8. Taber, R., Yager, R.R., Helgason, C.M.: Quantization effects on the equilibrium behavior of combined fuzzy cognitive maps. Int. J. Intell. Syst. 22(2), 181–202 (2006)

    Article  Google Scholar 

  9. Tsadiras, A.K., Margaritis, K. G.: Cognitive mapping and certainty neuron fuzzy cognitive maps. Inf. Sci. 101, 109–130 (1997)

    Google Scholar 

  10. Eberhart, R.C., Dobbins, R.W.: Neural Network PC Tools. Academic Press, San Diego (1990)

    Google Scholar 

  11. Tsadiras, A.K., Margaritis, K.G.: Recursive certainty neurons and an experimental study of their dynamical behaviour. In: Proceedings of the European Congress on Intelligent Techniques and Soft Computing (EUFIT ’97), Aachen, pp. 510–515 (1997)

    Google Scholar 

  12. Buchanan, B.G., Shortliffe, E.H.: Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley, Boston (1984)

    Google Scholar 

  13. Papageorgiou, E.I.: Learning algorithms for fuzzy cognitive maps-a review study. IEEE Trans. Syst. Man Cybern. (SMC)-Part C 42(2), 150–163 (2012)

    Google Scholar 

  14. Papakostas, G.A., Koulouriotis, D.E., Polydoros, A.S., Tourassis, V.D.: Towards Hebbian learning of fuzzy cognitive maps in pattern classification problems. Expert Syst. Appl. 39(12), 10620–10629 (2012)

    Article  Google Scholar 

  15. Stach, W., Pedrycz, W., Kurgan, L.A.: Learning of fuzzy cognitive maps using density estimate. IEEE Trans. Syst. Man Cybern. Part B Cybern. 42(3), 900–912 (2012)

    Article  Google Scholar 

  16. Cheah, W.P., Kim, Y.S., Kim, K.Y., Yang, H.J.: Systematic causal knowledge acquisition using FCM constructor for product design decision support. Expert Syst. Appl. 38(12), 15316–15331 (2011)

    Article  Google Scholar 

  17. Jose, A., Contreras, J.: The FCM designer tool in “Fuzzy Cognitive Maps”. Stud. Fuzziness Soft Comput. 247, 71–87 (2010)

    Article  Google Scholar 

  18. Dickerson, J.A., Berleant, D., Cox, Z., Qi, W., Wurtele, E.: Creating Metabolic Network Models using Text Mining and Expert Knowledge, A tlantic Symposium on Molecular Biology and Genome Information Systems and Technology (CBGIST 2001). Durham, North Carolina (2001)

    Google Scholar 

  19. Jung, J.J.: Semantic annotation of cognitive map for knowledge sharing between heterogeneous businesses. Expert Syst. Appl. 39, 5857–5860 (2012)

    Article  Google Scholar 

  20. 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)

    Article  MathSciNet  Google Scholar 

  21. Tsadiras, A.K., Bassiliades, N.: RuleML representation and simulation of fuzzy cognitive maps. Expert Syst. Appl. 40, 1413–1426 (2013)

    Article  Google Scholar 

  22. Bratko I.: Prolog-Programming for Artificial Intelligence, 3rd edn, Addison Wesley, Boston (2000)

    Google Scholar 

  23. Tsadiras, A.K., Margaritis, K.G.: An experimental study of dynamics of the certainty neuron fuzzy cognitive maps. NeuroComputing 24, 95–116 (1999)

    Google Scholar 

  24. Fuchs, N.E., Kaljurand, K., Schneider, G.: Attempto controlled english meets the challenges of knowledge representation. Reasoning, interoperability and user interfaces. In: FLAIRS Conference 2006, 664–669 (2006)

    Google Scholar 

  25. Kontopoulos, E., Bassiliades, N., Antoniou, G., Seridou, A.: Visual modeling of defeasible logic rules with DR-VisMo. Int. J. Artif. Intell. Tools 17(5), 903–924 (2008)

    Article  Google Scholar 

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Correspondence to Athanasios Tsadiras .

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Tsadiras, A., Bassiliades, N. (2014). Using RuleML for Representing and Prolog for Simulating Fuzzy Cognitive Maps. In: Papageorgiou, E. (eds) Fuzzy Cognitive Maps for Applied Sciences and Engineering. Intelligent Systems Reference Library, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39739-4_4

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  • DOI: https://doi.org/10.1007/978-3-642-39739-4_4

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