Abstract
Conventional adaptive systems have common well-known constraints when attempting to normalize environment. An adaptive system must contain a certain number of rules allowing such a system to adapt to specific situations. If there is an absence of a rule in a new situation, the system cannot take appropriate action. Building and managing such complex static adaptive systems places an enormous burden on system developers. In this paper, we propose a multi-agent based intelligent adaptive system with a self-growing engine. In this system, the inference agent evaluates input context with specific factors and analyzes the results. The decision agent selects the most appropriate action among alternatives available for a specific context and intelligently evolves and adapts by means of a self-growing engine (SGE). The SGE can evaluate actions and generate new rules by applying it to a practical situation using remote video conferencing with mobile devices such as PDAs, and PCs.
This work was supported by the Ubiquitous Autonomic Computing and Network Project, 21st Century Frontier R&D Program in Korea and the Brain Korea 21 Project in 2004. Dr. E. Lee is the corresponding author
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© 2005 Springer-Verlag Berlin Heidelberg
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Lee, S., Oh, J., Lee, E. (2005). An Architecture for Multi-agent Based Self-adaptive System in Mobile Environment. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_64
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DOI: https://doi.org/10.1007/11508069_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
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