Towards an Agent-Based Knowledge Management System - Developing Concepts in Applied Intelligence - page 99

4. Measuring the Impact of Parameters' Weight on Results: We have run 10 dif-
ferent trials, on each parameter while decreasing its value such as 25, 20, …, 0 for
course_name of a random problem. The other parameters are kept fixed. Figure 8
above shows the results that RA extracts are affected by the weight of
course_name. When the weight increased the number of extracted cases increased.
6 Concluding Remark
We have, in this paper, proposed a KMS that employs agents and CBR to extract tacit
knowledge and support OL and decision making. The experimental results, using
various measurement methods, are reliable and efficient. We compared the results of
the system with that of a committee of 7 experts from CBJ. The results were very
positive. Out of 37 cases, there was a match on the 1 st choice of 23 cases, 12 on the
2 nd choice and 2 on the 3 rd choice made by the system. Furthermore, the experts indi-
cate that the system has indeed the ability to learn.
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