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

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.

• Type: Conference
• Year: 2009
• Where: IDEAL
• Authors: Rosa Cano, Angélica González, Juan Francisco de Paz, Sara Rodríguez
• Keywords: Architecture Incorporates Cbr-agents, Atmosphere Exchange Carbon, Data Mining, IDEAL 2009, Multiagent Architecture