AI for Systems Engineering Assessment Model (SEAM) activities

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. Applicants 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. Applicants are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: The Department of Defense requires programs to implement sound systems engineering practices. The Air Force utilizes the Systems Engineering Assessment Model (SEAM) to promote the application and use of standard systems engineering processes across the Air Force and improve performance of the processes within programs. The Air Force Nuclear Weapons Center would like an Artificial Intelligence or other software application that will review program documents, products and models and generate metrics that describe how well the artifacts meet key tenets of Air Force SEAM process. In addition, the center desires to have the application generate reports correlating strengths and weaknesses of the artifacts where they adequately address Air Force policies and areas where the documents/models do not fully comply with policy, and recommendations for improvement. DESCRIPTION: The Air Force Nuclear Weapons Center would like a contractor to develop an application that will be hosted on a government network and used to review multiple process models or documents in accordance with the government's Systems Engineering Assessment Model (SEAM) practices. The contractor will work with the government to document metrics. The contractor will hold at least quarterly technical interchange meetings with the government to review progress and work through issues. The contractor will also hold monthly status meetings with the government and provide status reports. The contractor will perform work with the government security teams to ensure the application can be installed on the government network. Before completion, the government would like training on the delivered application, a final demonstration using government furnished documents and process models, source code and technical documentation as well as any cybersecurity related documents for the delivered product. PHASE I: This is a D2P2 topic, and as such, no Phase I awards will be made. As part of the "Phase I-type" feasibility demonstration, applicants shall provide evidence of their firms' experience developing AI/ML applications that can perform similar tasks. A report of at least one similar application describing the application, solution and scale of effort shall be included. PHASE II: Develop and deliver an AI application that will be hosted on a government system and used to review AFNWC program artifacts and generate reports. The contractor will deliver training materials, software and supporting documentation as well perform one formal training session for up to 25 students. PHASE III DUAL USE APPLICATIONS: The effort can be expanded to review program technical documents and models and perform assessments based on design review criteria (e.g. SRR, PDR, CDR, etc...). The information and materials provided pursuant to or resulting from this topic are restricted under the ITAR, 22 C.F.R. Parts 120 - 130 or the EAR, 15 C.F.R. Parts 710 - 774. REFERENCES: 1. Air Force Systems Engineering Assessment Model Management Guide, Version 2. Air Force Inst of Tech, Wright-Patterson AFB, OH, USA, Sep 2010. Accessed: Aug, 18, 2022. [Online]. Available: https://apps.dtic.mil/sti/pdfs/ADA538786.pdf KEYWORDS: Artificial Intelligence; machine learning; systems engineering assessment model; SEAM