Using Artificial Neural Networks for Predicting Vehicle Survivability within a Virtual Simulation

A unique contribution the U.S. Army currently provides is what is known as Virtual Experiments (VEs). A VE consists of a large group of active-duty soldiers who participate in a video game simulating a battlefield scenario. During these simulations, the soldiers are provided with novel protective vehicle capabilities in an effort to evaluate their effectiveness on the battlefield. However, these VEs take a significant amount of time to conduct and are expensive. Using Artificial Neural Networks (ANNs) this study looks to predict vehicle survivability based on a limited amount of VE data. The results entail an overall predictive accuracy of 76.8% using only two ANN input features and provides a framework for the eventual addition of more VE datasets.