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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References
Chan, A.T.S., Chuang, S.-N.: MobiPADS: A Reflective Middleware for Context- Aware Mobile Computing. IEEE Transaction on Software Engineering 29(12), 1072–1085 (2003)
Friday, A., Davies, N., Blair, G.S., Cheverst, K.W.J.: Developing Adaptive Applications: The MOST Experience. Journal of Integrated Computer-Aided Engineering 6(2), 143–157 (1999)
Noble, B.: System Support for Mobile, Adaptive Applications. IEEE Personal Communications 7(1) (February 2000)
Lum, W.Y., Lau, F.C.M.: User-Centric Content Negotiation for Effective Adaptation Service in Mobile Computing. IEEE Transaction on Software Engineering 29(12), 1100–1111 (2003)
W3C – Composite Capability/Preference Profiles (CC/PP) (2004), http://www.w3.org/Mobile
Poladian, V., Sousa, J.P., Garlan, D., Shaw, M.: Dynamic Configuration of Resource-Aware Services. In: 26th International Conference on Software Engineering (ICSE 2004), May 2004, pp. 604–613 (2004)
Schilit, B.N., Hilbert, D.M., Trevor, J.: Context-aware communication. IEEE Wireless Communications 9(5), 46–54 (2002)
Bellavista, P., Corradi, A., Montanari, R., Stefanelli, C.: Context-Aware Middleware for Resource Management in the Wireless Internet. IEEE Transactions on Software Engineering 29(12) (December 2003)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning), March 1998. The MIT Press, Cambridge (1998)
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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
Online ISBN: 978-3-540-31693-0
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