2024-03-29T05:23:32Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1349462022-02-07T15:36:00Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134811
2017-09-06T09:15:06Z
urn:hdl:10366/134946
A Multi-agent System to Learn from Oceanic Satellite Image Data
Cano, Rosa
González Arrieta, María Angélica
Paz Santana, Juan Francisco de
Rodríguez González, Sara
Computer Science
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.
2017-09-06T09:15:06Z
2017-09-06T09:15:06Z
2009
info:eu-repo/semantics/article
Intelligent Data Engineering and Automated Learning - IDEAL 2009 Lecture Notes in Computer Science. Lecture Notes in Computer Science. Volumen 5788, pp. 727-734.
978-3-642-04393-2 (Print) / 978-3-642-04394-9 (Online)
0302-9743 (Print) / 1611-3349 (Online)
http://hdl.handle.net/10366/134946
en
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivs 3.0 Unported
Springer Science + Business Media