Advertisement

Towards an Intelligent Decision Making Support

  • Nesrine Ben Yahia
  • Narjès Bellamine
  • Henda Ben Ghezala
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 182)

Abstract

This paper presents an intelligent framework that combines case-based reasoning (CBR), fuzzy logic and particle swarm optimization (PSO) to build an intelligent decision support model. CBR is a useful technique to support decision making (DM) by learning from past experiences. It solves a new problem by retrieving, reusing, and adapting past solutions to old problems that are closely similar to the current problem. In this paper, we combine fuzzy logic with case-based reasoning to identify useful cases that can support the DM. At the beginning, a fuzzy CBR based on both problems and actors’ similarities is advanced to measure usefulness of past cases. Then, we rely on a meta-heuristic optimization technique i.e. Particle Swarm Optimization to adjust optimally the parameters of the inputs and outputs fuzzy membership functions.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Simon, H.: The New science of management decision. Prentice Hall, Englewood Cliffs (1977)Google Scholar
  2. 2.
    Zaraté, P.: Des Systèmes Interactifs d’Aide la Décision Aux Systèmes Coopératifs d’Aide la Décision: Contributions conceptuelles et fonctionnelles. HDR dissertation, INP Toulouse (2005)Google Scholar
  3. 3.
    Riesbeck, C.K., Schank, R.C.: Inside Case-Based Reasoning. Lawrence Erlbaum Associates, New Jersey (1989)Google Scholar
  4. 4.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and system approaches. AI Communications 7, 39–59 (1994)Google Scholar
  5. 5.
    Main, J., Dillon, T.S., Khosla, R.: Use of fuzzy feature vectors and neural vectors for case retrieval in case based systems. In: Biennial Conference of the North American Fuzzy Information Processing Society, NAFIPS 1996, pp. 438–443. IEEE, New York (1996)CrossRefGoogle Scholar
  6. 6.
    Zadeh, L.A.: Fuzzy Logic = Computing with Words. IEEE Transactions on Fuzzy Systems 4(2), 103–111 (1996)MathSciNetCrossRefGoogle Scholar
  7. 7.
    ShengZhou, Y., Lai, L.Y.: Optimal design for fuzzy controllers by genetic algorithms. IEEE Transactions on Industry Applications 36(1), 93–97 (2000)CrossRefGoogle Scholar
  8. 8.
    Parsopoulos, K.E., Vrahatis, M.N.: Particle Swarm Optimization and Intelligence: Advances and Applications. Information Science Reference (an imprint of IGI Global), United States of America (2010)CrossRefGoogle Scholar
  9. 9.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, California (1993)Google Scholar
  10. 10.
    Jeng, B.C., Liang, T.P.: Fuzzy indexing and retrieval in case-based systems. Expert Systems with Applications 8(1), 135–142 (1995)CrossRefGoogle Scholar
  11. 11.
    Eberhart, R.C., Kennedy, J.: New optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)Google Scholar
  12. 12.
    Yisu, J., Knowles, J., Hongmei, L., Yizeng, L., Kell, D.B.: The Landscape Adaptive Particle Swarm Optimizer. Applied Soft Computing 8, 295–304 (2008)CrossRefGoogle Scholar
  13. 13.
    Clerc, M.: The Swarm and the Queen: Towards A Deterministic and Adaptive Particle Swarm Optimization. In: Proceedings of the Congress of Evolutionary Computation, Washington, DC, pp. 1951–1957 (1999)Google Scholar
  14. 14.
    Carlisle, A., Dozier, G.: An Off-The-Shelf PSO. In: Proceedings of the Particle Swarm Optimization Workshop, Indianapolis, Ind., USA, pp. 1–6 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nesrine Ben Yahia
    • 1
  • Narjès Bellamine
    • 1
  • Henda Ben Ghezala
    • 1
  1. 1.RIADI LaboratoryNational School of Computer Sciences, University Campus ManoubaManoubaTunisia

Personalised recommendations