Knowledge-Based Randomization for Amplification

Case-Based Reasoning (CBR) is concerned with knowledge representation and processing to resolve new problems based on previous experiences. When associated with a mechanism for knowledge amplification, the resolution process is enhanced. The work presented in this paper pertains to knowledge amplification based on randomization for problem resolution. New knowledge is deduced from hidden knowledge by transformation. The approach is applied to a route planning application, thus highlighting its strength in enhancing the CBR system by inferring pertinent new and valid knowledge. It is validated based on domain user expertise.