MDA27BZ02-DV010 — Multi-Fidelity Transfer Learning for Defense Applications

Use new and evolving Artificial Intelligence (AI) training techniques, algorithms, and protocols to rapidly reduce the resources required to integrate AI into the Missile Defense System (MDS).

Rapid Artificial Intelligence (AI) integration into the MDS is required for the United States to properly defend itself against its adversaries. The development of robust Artificial Intelligence/Machine Learning (AI/ML) agents for complex missile defense and aerospace applications is often constrained by the computational cost, limited availability, and security restrictions of high-fidelity simulation environments. Transfer learning provides a critical pathway to overcoming these barriers. By initially training agents on low-fidelity models or unclassified representations—where data is abundant and computational demands are reduced—core behaviors, strategies, and decision-making frameworks could be efficiently developed and refined. These pretrained models could then be adapted and optimized in high-fidelity and/or environments at higher classification levels, allowing the agent to leverage its foundational knowledge while rapidly acquiring the nuanced performance required for operational deployment. This staged approach significantly reduces training time, minimizes resource expenditure, and mitigates the risk of overfitting to scarce high-fidelity/classified data. Moreover, it enables iterative development cycles where low-fidelity environments serve as agile testbeds for innovation, and high-fidelity environments refine and validate for mission-readiness. Leveraging transfer learning in this manner not only accelerates capability delivery but also ensures that AI/ML systems achieve the precision, resilience, and adaptability necessary for real-world mission success.

Phase I-like proposals will not be evaluated and will be rejected as nonresponsive. For this topic, the Government expects the small business would have accomplished the following in a Phase I-like effort via some other means, e.g., independent research and development (IRAD) or other source, a concept for a workable prototype or design to address, at a minimum, the basic capabilities of the stated objective above. Proposal must show, as appropriate, a demonstrated technical feasibility or nascent capability. The documentation provided must substantiate the proposer’s development of a preliminary understanding of the technology to be applied in their Phase II proposal in meeting topic objectives. Documentation should comprise all relevant information including, but not limited to, technical reports, test data, prototype designs/models, and performance goals/results. Feasibility = maturity and what have you already done/validated. Proposers interested in participating in Direct to Phase II must include in their responses to this topic Phase I feasibility documentation that substantiates the scientific and technical merit and Phase I feasibility described in Phase I above has been met. (i.e., the small business must have performed a proof of concept like “Phase I” component and/or other validation in a relevant environment, and/or at a much higher Technology Readiness Level (TRL) level (5 or higher) and describe the potential commercialization applications. The documentation provided must validate that the proposer has completed development of technology in previous work or research completed.) IRAD work: Documentation should include the most relevant information including, but not limited to: technical reports, test data, prototype designs/models, and/or performance goals/results. Work submitted within the feasibility documentation must have been substantially performed by the proposer and/or the principal investigator (PI).

Create a full prototype capability implementing the tools and demonstrating the processes required to enable AI transfer learning for MDS applications. Design and conduct a case study using the prototype implementation with representative data and models. Key Technical Objectives: -Establish a low fidelity training environment based on limited data for AI agent learning applicable to MDS use cases. -Develop, apply, and implement transfer learning methodologies to drive transfer learning. -Develop and integrate high fidelity representations of MDS simulation environment across multiple levels of classification. -Demonstrate resource savings of transfer learning methods versus traditional training methods. -Design and implement a human-machine interface to seamlessly orchestrate the AI training workflows. -Deliver prototype AI training workflows for mission relevant use cases that integrate with existing MDS agent training systems.

Scale up the capability from the prototype utilizing the new processes and software technologies developed in Phase II into a mature, fieldable capability. Work with additional DoD or commercial organizations to integrate the technology into a system level testbed for analyst use.