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Knowledge management for computational intelligence systems
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1860/1744
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| 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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