The proliferation of intelligent systems in today's military demands increased focus on the optimization of human-robot
interactions. Traditional studies in this domain involve large-scale field tests that require humans to operate semiautomated
systems under varying conditions within military-relevant scenarios. However, provided that adequate
constraints are employed, modeling and simulation can be a cost-effective alternative and supplement. The current
presentation discusses a simulation effort that was executed in parallel with a field test with Soldiers operating military
vehicles in an environment that represented key elements of the true operational context. In this study, "constructive"
human operators were designed to represent average Soldiers executing supervisory control over an intelligent ground
system. The constructive Soldiers were simulated performing the same tasks as those performed by real Soldiers during
a directly analogous field test. Exercising the models in a high-fidelity virtual environment provided predictive results
that represented actual performance in certain aspects, such as situational awareness, but diverged in others. These
findings largely reflected the quality of modeling assumptions used to design behaviors and the quality of information
available on which to articulate principles of operation. Ultimately, predictive analyses partially supported expectations,
with deficiencies explicable via Soldier surveys, experimenter observations, and previously-identified knowledge gaps.