Abstract
This paper presents an architecture that makes it possible to construct dynamic systems capable of growing in dimension and adapting its knowledge to environmental changes. An architecture must define the components of the system (agents in this case), as well as the way in which those components communicate and interact with each other in order to achieve the system’s goals. The work presented here focuses on the development of an agent-based architecture, based on the use of deliberative agents, that incorporate case based reasoning. The proposed architecture requires an analysis and design methodology that facilitates the building of distributed systems using this technology. The proposal combines elements of existing methodologies such as Gaia and AUML in order to take advantage of their characteristics. Moreover the architecture takes into account the possibility of modelling problems in dynamic environments and therefore the use of autonomous models that evolve over time. To solve this problem the architecture incorporates CBR-agents whose aim is to acquire knowledge and adapt themselves to environmental changes. The architecture has been applied to model for evaluating the interaction between the atmosphere and the ocean, as well as for the planification and optimization of sea routes for vessels. The system has been tested successfully, and the results obtained are presented in this paper.
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Bajo, J., Corchado, J.M. (2006). Multiagent Architecture for Monitoring the North-Atlantic Carbon Dioxide Exchange Rate. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science(), vol 4177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881216_34
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DOI: https://doi.org/10.1007/11881216_34
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