Using machine learning techniques to interpret results from discrete event simulation

This paper describes an approach to the interpretation of discrete event simulation results using machine learning techniques. The results of two simulators were processed as machine learning problems. Interpretation obtained by the regression tree learning system Retis was intuitive but obviously expressed in a complicated way. To enable a more powerful knowledge representation, Inductive Logic Programming (ILP) system Markus was used, that also highlighted some attribute combinations. These attribute combinations were used as new attributes in further experiments with Retis and Assistant. Some interesting regularities were thereby automatically discovered.