Integrating multi-case-base-reasoning with distributed case-based reasoning

Case-based analogical reasoning is a pedagogical technique that improves problem solving by helping learners identify a common structural principle shared among multiple cases. Identification and transfer of the shared principle facilitates solving novel problems or patient cases. When cueing is coupled with the process, transfer of the structural principle to the problem is enhanced. The case-based reasoning supports lazy learning through exploitation of past problems solution's in solving original complication. In this paper, the multi-case-base reasoning is being integrated with distributed case-based reasoning approach to enhance the performance of the case-based reasoning application. Various retrieval strategies are implemented to find the similar cases from the case base. This model computes performance of the conventional case-based reasoning approach.