Representing intelligent decision making in discrete event simulation : a stochastic neural network approach

Abstract

The problem of representing decision making behaviour in discrete event simulation was investigated. Of particular interest was modelling variety in the decisions, where different people might make different decisions even where the same circumstances hold. An initial investigation of existing and alternative approaches for representing decision making was carried out. This led to the suggestion of using a neural network to represent the decision making behaviour in the form of a multi-criteria probability distribution based on data of observed decision making. The feasibility of the stochastic neural network approach was investigated. Models were fitted using artificial data from discrete and continuous distributions that included the shape parameters as inputs, and tested against known results from the distributions. Also a bank simulation was used to collect data from volunteers who controlled the queuing decisions of customers inside the bank. Models of their behaviour were created and implemented in the bank simulation to automate the decision making of customers. The investigation established the feasibility of the approach, although it indicated the need for substantial amounts of data showing examples of decision making. A hybrid model that combined the stochastic neural network approach with a rule-based approach allowed the development of more general models of decision making behaviour