Advertisement

A Contingency Response Multi-agent System for Oil Spills

  • Aitor Mata
  • Dante I. Tapia
  • Angélica González
  • Belén Pérez
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 55)

Abstract

This paper presents CROS, a contingency response multi-agent system for oil spills situations. The system makes use of a Case-Based Reasoning system which generates predictions to determine the probability of finding oil slicks in certain areas of the ocean. CBR uses past information to generate new solutions to the current problem. The system 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 has been used to predict real oil spill situations. Results have demonstrated that the system can accurately predict the presence of oil slicks in determined zones. It has been demonstrated that using a distributed architecture can enhance the overall performance of the system.

Keywords

Oil Spill Multi-Agent Systems Case-Based Reasoning Distributed Architectures 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Menemenlis, D., Hill, C., Adcroft, A., Campin, J.M., et al.: NASA Supercomputer Improves Prospects for Ocean Climate Research. EOS Transactions 86(9), 89–95 (2005)CrossRefGoogle Scholar
  4. 4.
    Palenzuela, J.M.T., Vilas, L.G., Cuadrado, M.S.: Use of ASAR images to study the evolution of the Prestige oil spill off the Galician coast. International Journal of Remote Sensing 27(10), 1931–1950 (2006)CrossRefGoogle Scholar
  5. 5.
    Solberg, A.H.S., Storvik, G., Solberg, R., Volden, E.: Automatic detection of oil spills in ERS SAR images. IEEE Transactions on Geoscience and Remote Sensing 37(4), 1916–1924 (1999)CrossRefGoogle Scholar
  6. 6.
    Ross, B.J., Gualtieri, A.G., Fueten, F., Budkewitsch, P., et al.: Hyperspectral image analysis using genetic programming. Applied Soft Computing 5(2), 147–156 (2005)CrossRefGoogle Scholar
  7. 7.
    Corchado, J.M., Fdez-Riverola, F.: FSfRT: Forecasting System for Red Tides. Applied Intelligence 21, 251–264 (2004)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Cerami, E.: Web Services Essentials Distributed Applications with XML-RPC, SOAP, UDDI & WSDL. O’Reilly & Associates, Inc., Sebastopol (2002)Google Scholar
  10. 10.
    Gunter, S., Schraudolph, N.N., Vishwanathan, S.V.N.: Fast Iterative Kernel Principal Component Analysis. Journal of Machine Learning Research 8, 1893–1918 (2007)MathSciNetGoogle Scholar
  11. 11.
    Fritzke, B.: Growing cell structures—a self-organizing network for unsupervised and supervised learning. Neural Networks 7(9), 1441–1460 (1994)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Plaza, E., Armengol, E., Ontañón, S.: The Explanatory Power of Symbolic Similarity in Case-Based Reasoning. Artificial Intelligence Review 24(2), 145–161 (2005)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Aitor Mata
    • 1
  • Dante I. Tapia
    • 1
  • Angélica González
    • 1
  • Belén Pérez
    • 1
  1. 1.Departamento Informática y AutomáticaUniversidad de Salamanca, Plaza de la Merced s/n, 37008, Salamanca, Spain University of SalamancaSpain

Personalised recommendations