Wrong in the Right Way: Balancing Realism Against Other Constraints in Simulation-Based Training

Abstract

There is an unspoken but pervasive assumption that the human behavior representations which populate simulation-based training systems must exhibit realistic behavior if the training is to be effective. This assumption motivates the continual calls to improve the realism of computer-generated forces and reflects the concern that human behavior representations that are “brittle” or too predictable will lead to a negative transfer of training. The emphasis on realism as the panacea for simulation-based training has led many researchers to overlook significant issues in the development of human behavior representations for training. Chief among these is that it is ultimately an empirical matter to determine how much realism is needed to meet training objectives in any given situation. In this paper, we describe a development methodology that specifically balances the need for sufficient realism in simulation-based training against the competing requirements for affordable, tractable and effective human behavior representations. The essential feature of this method is a focus on representing behavior at a “tactical” level by way of a finite state machine. Although finite state models of human behavior are hardly novel, they are perspicuous representations that can be used to communicate the behavior of models to subject matter experts for review, and given the appropriate user interface, even modification. What is novel about our method is the ability to integrate the finite state models with other formalisms, allowing the incremental additions of fidelity where more sophisticated behavior is required.