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
Name
Ambion_Phantom Fellow_JSE AOMP Process - Cohort 12.pdf
Description
Technical Paper
Size
841.08 KB
Format
Adobe PDF
Checksum (MD5)
116aeb8b9c1f357702bd61adcaf991f2
Author(s)
Li, William
Johnson, Kevin
Picardo, Christopher
Ambion, Francis
Date Issued
September 10, 2025
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.
Subjects
Artificial Intelligence
LLSC
Lincoln Laboratory
Air Force Artificial Intelligence Accelerator
Joint Simulation Environment
Machine Learning
Metrics
Defense Modeling and Simulation
Modular Components
MIT Department
Lincoln Laboratory
Persistent DSpace Link
