A stochastic model for Case-Based Reasoning
This provides a theoretical framework for evaluating CBR systems, which is incremental as it applies existing stochastic methods to a known AI approach.
The paper tackles the problem of modeling the Case-Based Reasoning (CBR) process by introducing an absorbing Markov chain to analyze its steps, resulting in probabilities for each step during problem-solving and a measure for system efficiency.
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.