Case-based reasoning in computers and human cognition: a mathematical framework
Michael Gr. Voskoglou · International Journal of Machine Intelligence and Sensory Signal Processing · 2013
Case-based reasoning (CBR) is a recent approach to problem solving and learning for computers and humans.In this paper, we introduce a finite Markov chain on the main steps of the CBR process.Using this approach we succeed in calculating the probabilities for the CBR process to be at a certain step in a certain phase of the solution of a real-world problem and we obtain a measure of the effectiveness of a CBR system in solving similar new problems.Next, the steps of the CBR process are represented as fuzzy subsets of a set of linguistic labels characterising the success of the CBR process in each of the above steps.Thus, we build a fuzzy model for the representation of a CBR system and we use the total possibilistic uncertainty as a measure of its effectiveness in solving new related problems.Examples are also given to illustrate our results.