The Area-of-Measurable-Performance (AOMP) Method Standard as a Foundational Archetype for the Cyclical Enhancement of the State of the Art Joint Simulation Environment (JSE) Technology

Abstract

The Department of the Air Force (DAF) envisions the need to incorporate Artificial Intelligence and Machine Learning (AI/ML) models into novel systems it develops for the purpose of enhancing them to meet its primary goal of maintaining total air superiority [2]. There is currently a need for developing a standard process for the design of successful AI/ML models capable of enhancing the novel systems the DAF develops. In this white paper we introduce the Area of Measurable Performance (AOMP) Method Standard and apply it to the Joint Simulation Environment (JSE) Technology, a state of the art system of systems under test, to identify AOMPS and their modular requirements [3] and metrics that lead to the accurate characterization of modular AI/ML models through a process that offers a high degree of trust and reuse, resulting in a method standard that organically promotes the development of successful modular AI/ML models for use in the performance improvement of the JSE technology or other system of system(s) [4] under test.