N251-030 TITLE: Artificial Intelligence (AI)-Generated Domain Specific Model and Ontology

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 a concept for a comprehensive assessment methodology to automatically generate a surface Domain Specific Model (DSM) and a model of concepts and their relationships (i.e., ontology) from domain-related technical documentation and generate machine readable interface documentation (e.g., JSON, XML).

DESCRIPTION: The Integrated Combat System (ICS) operates independently and as part of a netted integrated force with shared sensors, Command and Control (C2), weapons, and communications. A surface ship can have over forty (40) system elements that have unique data models. This data must be normalized through a common ontology to ensure a common understanding that is useable for machine processing (e.g., Artificial Intelligence and Machine Learning [AI/ML]). Manual generation of this DSM is a daunting task that has yet to be successfully accomplished. Once a DSM is established, new sensors, weapons, and communications elements can be integrated with little to no changes to the integration software, thus reducing time and required acquisition funding.

In contemporary military operations, the synergy and interoperability of diverse combat systems are critical for mission success. However, achieving seamless integration remains a formidable challenge due to the disparate data formats and structures employed by various platforms. This SBIR topic proposes harnessing the power of AI to devise a unified common data model (CDM) tailored specifically for combat systems (i.e., DSM). By employing advanced ML algorithms, natural language processing (NLP) techniques, and ontological analysis, the Navy seeks a capability to automatically extract, analyze, and harmonize data schemas from multiple sources. The envisioned AI-driven CDM will serve as a foundational framework for standardizing data representation, facilitating real-time data exchange, and enhancing decision-making processes across heterogeneous combat environments. There is no commercial technology that can generate a combat system CDM from Navy technical documentation sources.

The solution will employ a comprehensive assessment methodology comprised of simulation-based testing, real-world data integration trials, and user feedback analysis to evaluate the effectiveness, efficiency, AI trustworthiness, and usability of the proposed AI-driven CDM in enhancing combat system interoperability. The results of this assessment will provide valuable insights into the practical implications and potential limitations of implementing AI technologies for combat system integration, thereby informing future research directions and operational strategies in military contexts.

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 the AI-based DSM that meets the parameters of the Description. Demonstrate the feasibility of the concept in meeting the Navy’s need by a combination of analysis, modeling, and simulation. The Phase I Option, if exercised, will include initial design specifications and capabilities description to build a prototype solution in Phase II.

PHASE II: Develop and deliver a prototype AI-based DSM based upon the results of Phase I. Demonstrate the prototype’s functionality through ingesting of data from various representative simulated combat system sensor, weapon, and/or communication elements provided by the government. Demonstrate the ability to modify resource settings and send controls.

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 a set of containerized applications that transform sensor, weapon, and communications data into the DSM and back into element unique interface specifications to send element unique settings and controls. Provide necessary product-level objective quality evidence to support product certification for use. It is anticipated that DSM can become a standard for future element developments, thus minimizing future data transformations.

Automated generation of a DSM using AI has application beyond military systems. Any industry where there are differences in terminology can use this technology to achieve commonality.

REFERENCES:

1. Murphy, A. and Moreland, J. "Integrating AI Microservices into Hard-Real-Time SoS to Ensure Trustworthiness of Digital Enterprise Using Mission Engineering." JIDPS, 25(1), 2021, pp. 38-54. https://dl.acm.org/doi/10.3233/JID-210013

2. Moore, Ryan. "PEO IWS X Program Overview and ICS Development." April 2022. 1100_ICS Engagement brief 03302022_SAS_Distro A.pdf

3. "Generative AI: Key Opportunities and Research Challenges." Carnegie Mellon University/Software Engineering Institute, 2023.

4. "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: Power of Artificial Intelligence; Diverse Combat System; Unique Interfaces; Domain Specific Model; DSM; Combat Management System; Natural Language Processing; Integrated Combat System; ICS

TPOC 1: Jason Hunter
717-919-9196
Email: [email protected]

TPOC 2: Michael Darnell
(540) 653-3253
Email: [email protected]

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