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Hellenic Conference on Artificial Intelligence

SETN 2008: Artificial Intelligence: Theories, Models and Applications pp 288–300Cite as

A Prolog Based System That Assists Experts to Construct and Simulate Fuzzy Cognitive Maps

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5138))

Abstract

The method of Fuzzy Cognitive Map (FCM) is a combination of Fuzzy Logic and Artificial Neural Networks that is heavily used by experts and scientists of a diversity of disciplines, for strategic planning, decision making and predictions. A system that would assist decision makers to represent and simulate their own developed Fuzzy Cognitive Maps would be highly appreciated by them, especially from those that do not possess adequate computer skills. In this paper, a Prolog based system is designed and implemented to assist experts to both construct and simulate of their own FCMs. The representation capabilities of the system and the design choices are discussed and a variety of examples are given to demonstrate the use of the system.

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References

  1. Billman, B., Courtney, J.F.: Automated Discovery in Managerial Problem Formulation: Formation of Causal Hypotheses for Cognitive Mapping. Decision Sciences 24, 23–41 (1993)

    Article  Google Scholar 

  2. Bougon, M.G.: Congregate Cognitive Maps: A Unified Dynamic Theory of Organization and Strategy. Journal of Management Studies 29, 369–389 (1992)

    Google Scholar 

  3. Çoban, O., Seçme, G.: Prediction of socio-economical consequences of privatization at the firm level with fuzzy cognitive mapping. Information Sciences: an International Journal 169(1-2), 131–154 (2005)

    MATH  Google Scholar 

  4. Papageorgiou, E., Groumpos, P.: A Weight Adaptation Method for Fuzzy Cognitive Maps to a Process Control Problem. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3037, pp. 515–522. Springer, Heidelberg (2004)

    Google Scholar 

  5. John, R.I., Innocent, P.R.: Modeling uncertainty in clinical diagnosis using fuzzy logic. IEEE Transactions on Systems, Man and Cybernetics 35(6), 1340–1350 (2005)

    Article  Google Scholar 

  6. Andreou, A.S., Mateou, N.H., Zombanakis, G.A.: The Cyprus Puzzle And The Greek - Turkish Arms Race: Forecasting Developments Using Genetically Evolved Fuzzy Cognitive Maps. Defence and Peace Economics 14(4), 293–310 (2003)

    Article  Google Scholar 

  7. Tsadiras, A.K., Kouskouvelis, I., Margaritis, K.G.: Using Fuzzy Cognitive Maps as a Decision Support System for Political Decisions. In: Manolopoulos, Y., Evripidou, S., Kakas, A.C. (eds.) PCI 2001. LNCS, vol. 2563, pp. 172–182. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Ramsey, D., Veltman, C.: Predicting the effects of perturbations on ecological communities: what can qualitative models offer? Journal of Animal Ecology 74, 905–916 (2005)

    Article  Google Scholar 

  9. Hobbs, B.F., Ludsin, S., Knight, R.L., Ryan, P.A., Biberhofer, J.: Fuzzy Cognitive Mapping as a Tool to Define Management Objectives for Complex Ecosystems. Ecological Applications 12(5) (October 2002)

    Google Scholar 

  10. Axelrod, R.: Structure of Design. Princeton University Press, Princeton (1976)

    Google Scholar 

  11. Kosko, B.: Fuzzy Cognitive Maps. Inter. Jour. of Man-Machine Studies 24, 65–75 (1986)

    Article  MATH  Google Scholar 

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

    MATH  Google Scholar 

  13. Khan, M.S., Chong, A., Gedeon, T.: A Methodology for Developing Adaptive Fuzzy Cognitive Maps for Decision Support. Journal of Advanced Computational Intelligence 4(6), 403–407 (2000)

    Google Scholar 

  14. Khan, M.S., Quaddus, M.: Group Decision Support Using Fuzzy Cognitive Maps for Causal Reasoning. Group Decision and Negotiation 13, 463–480 (2004)

    Article  Google Scholar 

  15. Tsadiras, A.K., Margaritis, K.G.: Cognitive Mapping and Certainty Neuron Fuzzy Cognitive Maps. Information Sciences 101, 109–130 (1997)

    Article  Google Scholar 

  16. Taber, R., Yager, R.R., Helgason, C.M.: Quantization effects on the equilibrium behavior of combined fuzzy cognitive maps. International Journal of Intelligent Systems 22(2), 181–202 (2006)

    Article  Google Scholar 

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

    MATH  MathSciNet  Google Scholar 

  18. Eberhart, R.C., Dobbins, R.W.: Neural Network PC Tools. Academic Press, London (1990)

    Google Scholar 

  19. 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 1997), Aachen, Germany, September 1997, pp. 510–515 (1997)

    Google Scholar 

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

    Google Scholar 

  21. Bratko, I.: Prolog–Programming for Artificial Intelligence, 3rd edn. Addison Wesley, Reading (2000)

    Google Scholar 

  22. Sharp, R.: CAT2: An Experimental Eurotra Alternative. Machine Translation 6, 215–228 (1991)

    Article  Google Scholar 

  23. Aho, V., Sethi, R., Ullman, J.D.: Compilers: Principles, Techniques and Tools. Addison-Wesley, Reading (1986)

    Google Scholar 

  24. Tsadiras, A.K., Margaritis, K.G.: An Experimental Study of Dynamics of the Certainty Neuron Fuzzy Cognitive Maps. NeuroComputing 24, 95–116 (1999)

    Article  Google Scholar 

  25. SWI-Prolog, Free Software Prolog environment, licensed under the Lesser GNU Public License, http://www.swi-prolog.org

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Authors and Affiliations

  1. Department of Information Technology, Alexander Technological Educational Institute of Thessaloniki, P.O.BOX 141, 57400, Thessaloniki, Greece

    Athanasios Tsadiras

Authors
  1. Athanasios Tsadiras
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Editor information

John Darzentas George A. Vouros Spyros Vosinakis Argyris Arnellos

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© 2008 Springer-Verlag Berlin Heidelberg

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Tsadiras, A. (2008). A Prolog Based System That Assists Experts to Construct and Simulate Fuzzy Cognitive Maps. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2008. Lecture Notes in Computer Science(), vol 5138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87881-0_26

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  • DOI: https://doi.org/10.1007/978-3-540-87881-0_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87880-3

  • Online ISBN: 978-3-540-87881-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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