A stochastic model for Case-Based Reasoning

Case-Bsed Reasoning (CBR) is a recent theory for problem-solving and learning in computers and people.Broadly construed it is the process of solving new problems based on the solution of similar past problems. In the present paper we introduce an absorbing Markov chain on the main steps of the CBR process.In this way we succeed in obtaining the probabilities for the above process to be in a certain step at a certain phase of the solution of the corresponding problem, and a measure for the efficiency of a CBR system. Examples are given to illustrate our results.

💡 Research Summary

This paper delves into Case-Based Reasoning (CBR), a recent theory for problem-solving and learning in both computers and humans. CBR involves solving new problems by referencing solutions to similar past problems. The authors introduce an absorbing Markov chain model on the key steps of the CBR process, which allows them to calculate the probabilities of being at certain stages during the resolution phase of corresponding problems. This approach also provides a measure for assessing the efficiency of a CBR system. By applying this stochastic model, the paper aims to enhance traditional CBR methods by considering various possibilities at each stage rather than simply referencing past solutions. The authors provide examples to illustrate their findings and demonstrate how this method can improve the overall effectiveness of problem-solving through CBR systems.