Neuro-fuzzy Modeling and Fuzzy Rule Extraction Applied to Conflict Management

  • Thando Tettey
  • Tshilidzi Marwala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


This paper outlines all the computational methods which have been applied to the conflict management. A survey of all the pertinent literature relating to conflict management is also presented. The paper then introduces the Takagi-Sugeno fuzzy model for the analysis of interstate conflict. It is found that using interstate variables as inputs, the Takagi-Sugeno fuzzy model is able to forecast conflict cases with an accuracy of 80.36%. Furthermore, it found that the fuzzy model offers high levels of transparency in the form of fuzzy rules. It is then shown how these rules can be translated in order to validate the fuzzy model. The Takagi-Sugeno model is found to be suitable for interstate modeling as it demonstrates good forecasting ability while offering a transparent interpretation of the modeled rules.


Fuzzy Rule Fuzzy Model Forecast Ability Interstate Variable IEEE International Joint 
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  1. 1.
    Beck, N., King, G., Zeng, L.: Improving quantitative studies of international conflict: A conjecture. American Political Science Review 94(1), 21–35 (2000)CrossRefGoogle Scholar
  2. 2.
    Jones, D., Bremer, S., Singer, J.: Militarized interstate disputes, 1816-1992 rationale, coding rules and empirical patterns. Conflict Management and Peace Science 15(2), 585–615 (1996)CrossRefGoogle Scholar
  3. 3.
    Gochman, C., Maoz, Z.: Militarized interstate disputes 1816-1976. Conflict resolution 28(4), 585–615 (1984)CrossRefGoogle Scholar
  4. 4.
    Correlates of war (Internet Listing) URL:
  5. 5.
    Lagazio, M., Russett, B.: A Neural Network Analysis of Militarised Disputes, 1885-1992: Temporal Stability and Causal Complexity. In: Temporal Stability and Causal Complexity, pp. 28–62. University of Michigan Press, New Jersey (2003)Google Scholar
  6. 6.
    Schrodt, P.: Patterns, rules and learning: Computational models of international behaviour (Unpublished manuscript available at) URL:
  7. 7.
    Oneal, J., Russet, B.: Prediction and classification with neural network models. Sociological Methods and Research 4(3), 499–524 (1999)Google Scholar
  8. 8.
    Mackay, D.: A practical bayesian framework for backpropagation networks. Neural Computing 4(3), 448–472 (1992)CrossRefGoogle Scholar
  9. 9.
    Marwala, T., Lagazio, M.: Modeling and controlling interstate conflict. In: Proceedings of the IEEE International Joint Conference on Neural Networks, Budapest, Hungary, pp. 1233–1238. IEEE, Los Alamitos (2004)Google Scholar
  10. 10.
    Habtemariam, E.A., Marwala, T.: Artificial intelligence for conflict management. In: Proceedings of the IEEE International Joint Conference on Neural Networks, Montreal, Canada, pp. 2583–2588. IEEE, Los Alamitos (2005)CrossRefGoogle Scholar
  11. 11.
    Jang, J., Sun, C., Mizutani, E.: Neuro-fuzzy and soft computing: A computational approach to Learning and Machine Intelligence, 1st edn. Prentice Hall, New Jersey (1997)Google Scholar
  12. 12.
    Babuska, R., Verbruggen, H.: Neuro-fuzzy methods for nonlinear sytem identification. Annual Reviews in Control 27, 73–85 (2003)CrossRefGoogle Scholar
  13. 13.
    Sentes, M., Ska, R.B., Kaymak, U., van Nauta Lemke, H.: Similarity measures is fuzzy rule based simplification. IEEE Transactions on Systems, Man and Cybernatics-Part B: Cybernatics 28(3), 376–386 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Thando Tettey
    • 1
  • Tshilidzi Marwala
    • 1
  1. 1.School of Electrical and Information EngineeringUniversity of the WitwatersrandJohannesburgSouth Africa

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