Knowledge acquisition for discrete event

Knowledge acquisition for an understanding of discrete event simulation systems is a diicult task. Machine Learning has been investigated to help in the knowledge acquisition process. Our approach involves consultation with a domain expert, and the use of discrete event simulation models and machine learning as tools for the intelligent analysis of simulated systems. Current methods for the analysis and interpretation of such systems are restricted to statistical techniques that say much about the reliability of an output, but little about the output inter connectivity. The objective of our work is to improve the ability to interpret the model to the level of explanation that might loosely be described as \How the simulated system works". The new interpretation techniques are based on knowledge acquisition concerning the system using machine learning tools. \How the simulated system works" is important for a decision maker's understanding of the system in terms of relationships between the various parameters, the utilisation of resources and the location of (potential) bottlenecks. This sort of understanding is important when managing queuing problems in production systems , or when planning a new system. The outputs from three discrete event simulators were processed as machine learning problems. In these experiments, the simulation outputs were interpreted by two attribute-based learning programs, namely Retis and Assistant, and by an Inductive Logic Programming system, namely Markus. The knowledge gained from the machine learning tools was evaluated by the domain expert , who considered that this knowledge contributed to an understanding of the system, and how it might prootably be modiied.