N251-035 TITLE: Integrated Air & Missile Defense (IAMD) Adaptive Trainer (IAT)

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

OBJECTIVE: Develop an Artificial Intelligence (AI) and Machine Learning (ML) (AI/ML) based immersive adaptive training approach that continuously improves an operator’s performance in evolving threat landscapes including Integrated Air & Missile Defense (IAMD).

DESCRIPTION: The Navy’s IAMD systems are crucial for protecting ships and assets from air and missile threats. However, the rapidly evolving nature of these threats necessitates continuous improvement in training methods to ensure personnel are prepared for the latest challenges. Traditional methods like classroom lectures and simulations often present predefined scenarios that may not reflect the ever-changing tactics and capabilities of adversaries. This repetitive nature can lead to complacency and hinder the development of critical thinking and adaptation skills needed in real-world situations. By addressing the limitations of traditional methods, improved training can equip Navy personnel with the adaptable skills and knowledge needed to counter sophisticated and evolving threats. Immersive adaptive training (IAT) can lead to significant cost savings through reduced training time, improved resource utilization, and increased operational efficiency.

The Navy seeks a next-generation, AI/ML based IAT system that can adapt to individual trainee needs, incorporate real-time data and feedback, provide immersive and engaging training experiences, and measure and track training effectiveness. There are currently no known commercial solutions to meet this technology need.

The technology should leverage AI/ML to assess individual trainee strengths and weaknesses, and tailor training content and exercises accordingly. This personalized approach can significantly improve learning outcomes compared to the one-size-fits-all methods currently being utilized.

It should integrate real-time data from simulations, exercises, and operational deployments to continuously update training scenarios and challenges. This approach ensures trainees are exposed to the most relevant and up-to-date threats.

It should utilize Virtual Reality (VR), Augmented Reality (AR), or other immersive technologies to create realistic and engaging training environments that enhance learning and retention. The technology should incorporate comprehensive performance metrics and feedback mechanisms to track individual and group progress, identify areas for improvement, and demonstrate the effectiveness of the adaptive training, improving an IAMD course of action (COA) by at least 10 percent.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by 32 U.S.C. § 2004.20 et seq., National Industrial Security Program Executive Agent and Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVSEA in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material during the advanced phases of this contract IAW the National Industrial Security Program Operating Manual (NISPOM), which can be found at Title 32, Part 2004.20 of the Code of Federal Regulations.

PHASE I: Develop a concept for an AI/ML-based IAT system for IAMD systems that meets the objectives stated in the Description. Demonstrate the feasibility of the concept in meeting the Navy’s need through a combination of analysis, modeling, and simulation. The Phase I Option, if exercised, will include the initial design specifications and capabilities description to build a prototype solution in Phase II.

PHASE II: Develop and demonstrate a prototype AI/ML-based IAT system for IAMD based on the results of Phase I. Demonstrate the prototype’s functionality based on pilot testing with representative groups of IAMD personnel to evaluate the system's effectiveness, usability, and impact on training outcomes. Develop a comprehensive training plan and supporting materials for the AI/ML-based adaptive training system.

It is probable that the work under this effort will be classified under Phase II (see Description section for details).

PHASE III DUAL USE APPLICATIONS: Support the Navy in transitioning the technology to Navy use. The final product will be an effective AI/ML-based IAT system for IAMD systems. Continued development will occur through testing with representative groups of IAMD personnel to evaluate the system's effectiveness, usability, and impact on training outcomes. Final system use will be analyzed by the Navy to determine effectiveness and utilization in IAMD systems. This system will provide state of the art training for IAMD by giving personalized, adaptable, and immersive learning experiences that significantly improve personnel readiness and combat effectiveness.

This technology could be utilized to support operator training in other mission areas. The AI/ML could additionally benefit commercial applications within the Federal Aviation Administration applications utilizing intensive motion to improve personnel experience benefiting proficiency.

REFERENCES:

1. Cooke, Nancy J. and Hilton, Margaret L. "Enhancing The Effectiveness of Team Science." Washington, DC: National Academies Press, 2015. https://nap.nationalacademies.org/download/19007

2. Harris, D.J., Arthur, T., Kearse, J., Olonilua, M., Hassan, E.K., De Burgh, T.C., Wilson, M.R. and Vine, S.J. "Exploring the role of virtual reality in military decision training." Frontiers in Virtual Reality, 27 March 2023. https://www.frontiersin.org/articles/10.3389/frvir.2023.1165030/full

3. Schirmer, Peter and Léveillé, Jasmin. "AI Tools for Military Readiness." Santa Monica, CA: RAND Corporation, 2021. https://www.rand.org/pubs/research_reports/RRA449-1.html

4. T"National Industrial Security Program Executive Agent and Operating Manual (NISP), 32 U.S.C. § 2004.20 et seq. (1993)." https://www.ecfr.gov/current/title-32/subtitle-B/chapter-XX/part-2004

KEYWORDS: Artificial Intelligence for training; Machine Learning for training; Adaptive Training; Integrated Air and Missile Defense; engaging training environments; Next-Generation Training; VR/AR

TPOC 1: Kimberly Elder
(540) 653-9677
Email: [email protected]

TPOC 2: Robert Lee
(540) 653-305
Email: [email protected]

---