Common Sense Reasoning in Diagnostic Systems - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 106

 Saving the newly made solution as part of a new case.
It is necessary to take into account that a solution on the basis of cases may not attain the
goal for the current situation, e.g., in the absence of a similar (analogous) case in the case
library. This problem can be solved if one presupposes in the CBR-cycle the possibility to
update the case library in the reasoning process (inference). A more powerful (in detecting
new facts or new information) method of reasoning by analogy is a method of updating case
libraries. We also note that the elements of case-based reasoning may be used successfully in
analogy-based reasoning methods; i.e., these methods successfully complement each other
and their integration in IDSS is very promising.

Fig. 5. The structure of CBR-cycle

Using the mechanism of cases for RT IDSS consists in issuing the decision to the operator
(DMP - Decision Making Person) for the current situation on the basis of cases which are
contained in a system. As a rule, the last stage in a CBR-cycle is excluded and performed by
the expert (DMP) because the case library should contain only reliable information
confirmed by the expert. Reconsidering and adaptation of the taken decision is required
seldom because the same object (subsystem) is considered.
The modified CBR-cycle for RT IDSS includes following stages:


Retrieving the closest (most similar) case (or cases) for the situation from the case
library;

 Using the retrieved case (precedent) for solving the current problem.

 Case-based reasoning for IDSS consists in definition of similarity degree of the current

situation with cases from a case library. For definition of similarity degree, the nearest
neighbor algorithm (k-nearest neighbor algorithm) is used [Eremeev et al., 2007a].