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MACSDE: Multi-Agent Contingency Response System for Dynamic Environments

  • Aitor Mata
  • Belén Pérez
  • Angélica González
  • Bruno Baruque
  • Emilio Corchado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5572)

Abstract

Dynamic environments represent a quite complex domain, where the information available changes continuously. In this paper, a contingency response system for dynamic environments called MACSDE is presented. The explained system allows the introduction of information, the monitoring of the process and the generation of predictions. The system makes use of a Case-Based Reasoning system which generates predictions using previously gathered information. It employs a distributed multi-agent architecture so that the main components of the system can be accessed remotely. Therefore, all functionalities can communicate in a distributed way, even from mobile devices. The core of the system is a group of deliberative agents acting as controllers and administrators for all functionalities. The system explained includes a novel network for data classification and retrieval. Such network works as a summarization algorithm for the results of an ensemble of Self-Organizing Maps. The presented system has been tested with data related with oil spills and forest fire, obtaining quite hopeful results.

Keywords

Dynamic environments Case-Based Reasoning oil spill forest fire Self Organizing Memory summarization 

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References

  1. 1.
    Corchado, J.M., Bajo, J., De Paz, Y., Tapia, D.I.: Intelligent Environment for Monitoring Alzheimer Patients, Agent Technology for Health Care. Decision Support Systems (in press) (2008)Google Scholar
  2. 2.
    Yang, J., Luo, Z.: Coalition formation mechanism in multi-agent systems based on genetic algorithms. Applied Soft Computing Journal 7(2), 561–568 (2007)CrossRefGoogle Scholar
  3. 3.
    Jayaputera, G.T., Zaslavsky, A.B., Loke, S.W.: Enabling run-time composition and support for heterogeneous pervasive multi-agent systems. Journal of Systems and Software 80(12), 2039–2062 (2007)CrossRefGoogle Scholar
  4. 4.
    Bratman, M.E., Israel, D., Pollack, M.E.: Plans and resource-bounded practical reasoning. Computational Intelligence 4, 349–355 (1988)CrossRefGoogle Scholar
  5. 5.
    Aamodt, A., Plaza, E.: Case-Based Reasoning: foundational Issues, Methodological Variations, and System Approaches. AI Communications 7(1), 39–59 (1994)Google Scholar
  6. 6.
    Corchado, J.M., Lees, B., Aiken, J.: Hybrid instance-based system for predicting ocean temperatures. International Journal of Computational Intelligence and Applications 1(1), 35–52 (2001)CrossRefGoogle Scholar
  7. 7.
    Carrascosa, C., Bajo, J., Julian, V., Corchado, J.M., et al.: Hybrid multi-agent architecture as a real-time problem-solving model. Expert Systems With Applications 34(1), 2–17 (2007)CrossRefGoogle Scholar
  8. 8.
    Fdez-Riverola, F., Iglesias, E.L., Díaz, F., Méndez, J.R., et al.: Applying lazy learning algorithms to tackle concept drift in spam filtering. Expert Systems With Applications 33(1), 36–48 (2007)CrossRefGoogle Scholar
  9. 9.
    Karayiannis, N.B., Mi, G.W.: Growing radial basis neural networks: merging supervised andunsupervised learning with network growth techniques. IEEE Transactions on Neural Networks 8(6), 1492–1506 (1997)CrossRefGoogle Scholar
  10. 10.
    Cerami, E.: Web Services Essentials Distributed Applications with XML-RPC, SOAP, UDDI & WSDL. O’Reilly & Associates, Inc. (2002)Google Scholar
  11. 11.
    Baruque, B., Corchado, E., Rovira, J., González, J.: Application of Topology Preserving Ensembles for Sensory Assessment in the Food Industry. In: Intelligent Data Engineering and Automated Learning - IDEAL 2008, pp. 491–497 (2008)Google Scholar
  12. 12.
    Corchado, E., Baruque, B., Yin, H.: Boosting Unsupervised Competitive Learning Ensembles. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4668, pp. 339–348. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Pölzlbauer, G.: Survey and Comparison of Quality Measures for Self-Organizing Maps. In: Paralic, J., Pölzlbauer, G., Rauber, A. (eds.) Fifth Workshop on Data Analysis (WDA 2004), pp. 67–82. Elfa Academic Press, London (2004)Google Scholar
  14. 14.
    Kohonen, T.: The Self-Organizing Map. Neurocomputing 21, 1–6 (1998)CrossRefzbMATHGoogle Scholar
  15. 15.
    Ros, F., Pintore, M., Chrétien, J.R.: Automatic design of growing radial basis function neural networks based on neighboorhood concepts. Chemometrics and Intelligent Laboratory Systems 87(2), 231–240 (2007)CrossRefGoogle Scholar
  16. 16.
    Plaza, E., Armengol, E., Ontañón, S.: The Explanatory Power of Symbolic Similarity in Case-Based Reasoning. Artificial Intelligence Review 24(2), 145–161 (2005)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Aitor Mata
    • 1
  • Belén Pérez
    • 1
  • Angélica González
    • 1
  • Bruno Baruque
    • 2
  • Emilio Corchado
    • 2
  1. 1.University of SalamancaSpain
  2. 2.University of BurgosSpain

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