Integrating Neural Networks and Semi‐Markov Processes for Automated Knowledge Acquisition: An Application to Real‐time Scheduling*

Abstract

ABSTRACT Recently, artificial neural networks (ANN) have gained attention as a promising modeling tool for building intelligent systems. A number of applications have been reported in areas varying from pattern recognition to bankruptcy prediction. In this paper, we present a creative methodology that integrates computer simulation, semi‐Markov optimization, and ANN techniques for automated knowledge acquisition in real‐time scheduling. The integrated approach focuses on the synergy between operations research and ANN in eliciting human knowledge, filtering inconsistent data, and building competent models capable of performing at the expert level. The new approach includes three main components. First, computer simulation is used to collect expert decisions. This step allows expert knowledge to be obtained in a non‐intrusive way and minimizes the difficulties involved in interviewing experts, constructing repertory grids, or using other similar structures required for manual knowledge acquisition. The data collected from computer simulation are then optimized using a semi‐Markov decision model to remove data redundancies, inconsistencies, and errors. Finally, the optimized data are used to build ANN‐based expert systems. The integrated approach is evaluated by comparing it with the human expert and using ANN alone in the domain of real‐time scheduling. The results indicate that ANN‐based systems perform worse than human experts from whom the data were collected, but the integrated approach outperforms human experts and ANN models alone.

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