A Multi-agent System to Learn from Oceanic Satellite Image Data

  • Rosa Cano
  • Angélica González
  • Juan F. de Paz
  • Sara Rodríguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)


This paper presents a multiagent architecture constructed for learning from the interaction between the atmosphere and the ocean. The ocean surface and the atmosphere exchange carbon dioxide, and this process is modeled by means of a multiagent system with learning capabilities. The proposed multiagent architecture incorporates CBR-agents to monitor the parameters that affect the interaction and to facilitate the creation of models. The system has been tested and this paper presents the results obtained.


CBR-BDI Air-Sea Monitoring Evaluation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rosa Cano
    • 1
  • Angélica González
    • 1
  • Juan F. de Paz
    • 1
  • Sara Rodríguez
    • 1
  1. 1.Departamento Informática y AutomáticaUniversidad de SalamancaSalamancaSpain

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