In this proposed effort, the team of Mosaic ATM and Aptima leverage their individual proven track records, as well as previous collaboration, to create the Reconfigurable Interactive Communication Training Environment (RICTE) using a combination of two primary technical approaches domain simulation modeling and deep-learning-based dialogue agents. The goal of the RICTE is to easily generate and evaluate text/chat and verbal communication in a training environment applied to the Air Traffic Control (ATC) domain, that can also be adapted to other domains. The proposed training system will utilize AI and ML technology to provide an intelligent, realistic, and autonomous communications software tool that provides relevant voice and text/chat information exchanges. The system will have the ability to provide synthesized speech in response to student voice communications, significantly improving scenario realism while simultaneously reducing instructor workload. The fidelity of this tool will be a significant improvement over current systems concerning realism, tailorability, instructor workload, and student learning. The system will be modular, scalable, flexible, and platform agnostic with potential for future expansion into a stand-alone capability within the classified arena.
Benefit: The Mosaic team understands Navy training and education needs, policies, processes, and organization. We will refine the concept, architecture, algorithms, and performance to support appropriate programs of record or other direct needs for this technology. Mosaic already provides systems and tools that are used extensively for ATC training in multiple domains of the civilian air transportation system. Our corporate strategic direction includes further expansion of these capabilities. We will leverage the RICTE as part of our existing system offerings to the FAA to support extensive ATC training capabilities, as well as commercial offerings to airlines. There is potential to use this system to train civilian ATC controllers and non-ATC specific controllers in the military such as Operations Specialist (OS) Air Intercept Controllers (AICs), E-2C/D Hawkeye AICs, E-3 AWACS AICs, and Joint Terminal Attack Controllers (JTACs) across all services and with allied and partner nations. The system has further potential to be utilized in the training of others who rely on communications in the execution of their duties. For example, the watchstanders who make up a carrier strike group (CSG) air and missile defense team (i.e., Air Defense Commander (AW), E-2D Hawkeye, CSG ships) could take full advantage of such a training tool.
Keywords: Air Traffic Control Training System Communications-based Training Training Fidelity Diagnostic Feedback Instructor Workload Artificial Intelligence (AI) & Machine Learning (ML) Natural Language Processing (NLP), Air Traffic Control Training System Communications-based Training Training Fidelity Diagnostic Feedback Instructor Workload Artificial Intelligence (AI) & Machine Learning (ML) Natural Language Processing (NLP)
