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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/1744

Title: Knowledge management for computational intelligence systems
Authors: Weber, Rosina O.
Wu, Duanqing
Issue Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Proceedings of the 8th IEEE International Symposium on High Assurance Systems Engineering, HASE 2004, pp. 116–125.
Abstract: Computer systems do not learn from previous experiences unless they are designed for this purpose. Computational intelligence systems (CIS) are inherently capable of dealing with imprecise contexts, creating a new solution in each new execution. Therefore, every execution of a CIS is valuable to be learned. We describe an architecture for designing CIS that includes a knowledge management (KM) framework, allowing the system to learn from its own experiences, and those learned in external contexts. This framework makes the system flexible and adaptable so it evolves, guaranteeing high levels of reliability when performing in a dynamic world. This KM framework is being incorporated into the computational intelligence tool for software testing at National Institute for Systems Test and Productivity. This paper introduces the framework describing the two underlying methodologies it uses, i.e. case-based reasoning and monitored distribution; it also details the motivation and requirements for incorporating the framework into CIS.
URI: http://hdl.handle.net/1860/1744
Appears in Collections:Faculty Research and Publications (IST)

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