Abstract
The development of complex self-adaptive systems has accelerated rapidly over the past decade, led by the Department of Defense, which has sought to develop and field military systems, such as unmanned aerial vehicles and unmanned ground vehicles, with elevated levels of autonomy to accomplish their mission with reduced funding and manpower. As their role increases, such systems must be able to adapt and learn, and make nondeterministic decisions. To field such systems requires extensive testing, evaluation, verification, and validation—a challenging task. To address this, we apply a novel systems perspective to develop a framework to predict future system performance based on the complexity of the operating environment using newly introduced complexity measures and learned costs. In this paper, we consider an autonomous military ground robot navigating in complex off-road environments. Using our model and experimental data from Defense Advanced Research Projects Agency-led experiments, we demonstrate the accuracy with which our model can predict system performance and then validate our model against other experimental results.
Published Version
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