A Machine Learning Algorithm to Estimate Minimal Cut and Path Sets from a Monte Carlo Simulation

In this paper a novel approach based on a machine learning algorithm (Hamming Clustering) is proposed to estimate the minimal cut and path sets, using the samples generated by a Monte Carlo simulation and any Evaluation Function. Two examples show the potential of the proposed approach.