The ScOPE solution â built on existing Boecore automation technologies â is an artificial intelligence (AI) / machine learning (ML) - powered workflow augmentation capability that shortens iterative, time intensive scenario planning processes to responsively exercise the missile defense architecture. The ScOPE solution will take test objectives and stakeholder requirements like the Scenario Planning Matrix and produce an optimized list of scenarios and data attributes and provide a visual verification and tailoring function in the form of an intermediate 3D graphical representation of generated scenarios. The resulting output in the form of scenarios that have been mapped across test objectives are then available to be included in the event Test Case Description Document (TCDD) that is provided to event participants for use to develop and integrate the required models that support the overall digital test infrastructure. AI/ML Deep Learning: Generating credible scenarios for missile defense is a complex problem that requires significant computing resources, data, and expert knowledge. Supervised Deep Learning provides a mechanism by using an artificial neural network where neurons and strengthening of pathways are modeled after the human brain to progressively learn what a credible scenario looks like. The resulting neural network with its embedded expert knowledge can then accurately âpredictâ previously unidentified credible scenarios given new input scenario requirements. The core aspects of the ScOPE optimization process that shorten scenario planning times are the Test Matrix Optimizer and the AI/ML-powered Expert Scenario Generator. The Test Matrix Optimizer leverages well-known Combinatorial Optimization techniques suited to solving similar problems. Approved for Public Release | 21-MDA-11013 (19
