Context-based representation of intelligent behavior in training simulations

This article presents, describes and evaluates a novel behavior representation paradigm that can effectively and efficiently be used to model the behavior of intelligent entities in a simulation. Called Context-based Reasoning (CxBR), this paradigm is designed to be applicable whenever simulation of human behavior is required. However, it is especially well suited to representing tactical behavior of opponents and teammates in simulation-based tactical training systems. Representing human behavior in a simulation is a complex and difficult task that generally requires significant investment in human effort as well as in computing resources. Conciseness and simplicity of representation and efficiency of computation, therefore, are important issues when developing models of intelligent opponents. We believe that this paradigm is an improvement over the rule-based approach, currently a common technique used in representing human behavior. We have preliminarily tested CxBR in two different prototype systems. Evaluation of the prototype shows that the context-based paradigm promises to meet the desired levels of simplicity, conciseness and efficiency required for the task. Copyright © 1998 The Society for Computer Simulation International