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CBR for modeling complex systems
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1860/2057
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| Title: | CBR for modeling complex systems |
| Authors: | Weber, Rosina O.
Proctor, Jason M.
Waldstein, Ilya
Kriete, Andres |
| Issue Date: | 2005 |
| Publisher: | Springer Verlag |
| Citation: | Paper presented at the Sixth International Conference on Case-Based Reasoning (ICCBR 2005); LNAI 3620: pp. 625-639. |
| Abstract: | This paper describes how CBR can be used to compare, reuse, and
adapt inductive models that represent complex systems. Complex systems are
not well understood and therefore require models for their manipulation and
understanding. We propose an approach to address the challenges for using
CBR in this context, which relate to finding similar inductive models
(solutions) to represent similar complex systems (problems). The purpose is to
improve the modeling task by considering the quality of different models to
represent a system based on the similarity to a system that was successfully
modeled. The revised and confirmed suitability of a model can become
additional evidence of similarity between two complex systems, resulting in an
increased understanding of a domain. This use of CBR supports tasks (e.g.,
diagnosis, prediction) that inductive or mathematical models alone cannot
perform. We validate our approach by modeling software systems, and illustrate
its potential significance for biological systems. |
| URI: | http://hdl.handle.net/1860/2057 |
| Appears in Collections: | Faculty Research and Publications (IST)
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