A. E. Henninger, A. J. Gonzalez, W. Gerber, M. Georgiopoulos, and R. F. DeMara,
 "On the Fidelity of SAFs: Performance Data,"in Proceedings of the 2000 
Interservice/Industry Training, Simulation and Education Conference (I/ITSEC-
2000), pp. 147 - 154, Orlando, Florida, U.S.A., November 27 - 30, 2000

Abstract:
A recent report developed by the National Research Council (NRC) for the Defense 
Modeling and Simulation Office (DMSO) encourages the use of real world, war-
gaming, and laboratory data in support of the development and validation of 
human behavioral models for military simulations. Also encouraged in this report 
is the use of interdisciplinary teams embracing the disciplines of the 
psychological, computer, and military sciences to create such models. This paper 
describes the use of an artificial intelligence modeling framework, 
observational learning, to support these objectives.
This framework combines the research methods of experimental psychology with the 
machine learning methods of computer science to develop behavioral models from 
data generated by military experts participating in live and/or simulated 
exercises.
To date, research has demonstrated that behavioral models developed through this 
framework can be integrated into popular Semi-Automated Force (SAF) systems to 
enhance their performance. However, there has been no known investigation as to 
what the benefits of this approach are with respect to behavioral model fidelity. 
This paper introduces the interdisciplinary nature of observational learning by 
briefly surveying its history with respect to computer science and psychology 
and by illustrating how it can be used in conjunction with military experts. 
Next, this paper examines experimental evidence to determine whether a 
significant difference exists between SAF performance and human performance for 
a low-level, skill task. Finally, this paper demonstrates how behavioral models 
developed through human performance data generated by military SMEs can be used 
in conventional SAF systems to make SAF performance more "human-like".