A stochastic model for Case-Based Reasoning
Michael Gr. Voskoglou
TL;DR
This paper introduces a stochastic Markov chain model for Case-Based Reasoning (CBR), providing probabilistic insights into the process steps and system efficiency, supported by illustrative examples.
Contribution
It presents the first probabilistic Markov chain model for CBR, enabling analysis of process step probabilities and system efficiency.
Findings
Derived probabilities for each CBR step
Quantified CBR system efficiency
Validated model with illustrative examples
Abstract
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.
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Taxonomy
TopicsAI-based Problem Solving and Planning · Constraint Satisfaction and Optimization · Logic, Reasoning, and Knowledge