Incubator Report: Digital Engineering Enhanced T&E of Learning-Based Systems

The broad objective of this incubator research is to develop approaches to the design of test and evaluation (T&E) programs and the acquisition of data/model rights for learning-based systems. The principal objective is to understand how increasing government access to the models and learning-agents used in designing next-generation military systems might decrease the need and expense of testing and increase confidence in results. Current approaches to test and evaluation (T&E) cannot address the challenges of identifying changes in operating conditions or adversarial actions that might cause the performance of an Artificial Intelligence/Machine Learning (AI/ML) model to deviate from design limits, particularly when considering autonomous functions that may engage in self-learning over the long life cycles of military systems. The research led by Principal Investigator Dr. Peter Beling (Virginia Tech), Co-Principal Investigators Dr. Laura Freeman (Virginia Tech) and Dr. Jitesh Panchal (Purdue University), posed the principal hypotheses that acquisition costs can be significantly reduced if T&E programs are based on the optimal balance between the cost of acquiring the technical data/algorithm rights of AI/ML systems and the cost of testing those systems.