State-dependent probabilistic model reduction for evaluation of human-robotic autonomous systems

A state-dependent model reduction approach to evaluating human-robotic system performance for the purpose of implementing Bayesian Networks is introduced. A procedure for reducing state dependencies on a specific class of operator failure events for probabilistic graph models is developed and verified with user data. In contrast to evaluating performance with respect to objective "workload" measurements, discrete failure "tag" events are classified and modeled as Bayesian network blocks with conditional dependencies on a subset of the total system states. Initial extraction of performance results are shown using data from the RoboFlag experiments.