DON26BZ01-NV023 — Risk-Aware Regenerative AI-based Multimodal Visual-Tactical (ISRT) (Observant-AI) – Monitor, Understand, Alert, and Assist

Award Maximum: $140,000 (Base) / $100,000 (Option) Period of Performance: 6 months (Base) + 6 months (Option) Phase Type: Phase I

OBJECTIVE: Develop risk-aware artificial intelligence (AI)-based computing methods motivated by three naval challenge problems that enable insightful active cross-domain (Sea-Space-Air-Land-Cyber) situational awareness and AI-assisted course of action and countermeasures in real-time conditions, namely, "LIVE" machine self-teaching (i.e., Regenerative AI); contextual machine exploitation; contextual networking to gain insights from accessible all-source-intelligence (ASI) and multimodal sensors; and proactive AI-assisted targeteer and decision support to manned and unmanned assets.

DESCRIPTION: The Observant-AI is envisioned as a distributed system of mission-focused AI agents that self-organize and share insights via ad hoc networking. The agents autonomously form mission-oriented collaborative teams to process and fuse multidomain anomalous events and activities for real-time AI-generated visual-tactical understanding, monitoring, alerts, and related operational risks. It applies natural language explanations for human-AI interactions, course of action assistance, and reasoning about risky engagements.

Problem scope and capability concerns: First, over the past three decades, advancements in AI and machine learning (ML) for applications in hybrid networked teaming of manned and unmanned systems and sensors have unlocked new possibilities across a range of naval operations for novel missions. On the other hand, the defensive and offensive effectiveness of these technologies against near-peer adversaries remains a significant challenge.

Second, current Naval ISRT operations follow rigorous protocols supported by wide-ranging wargaming scenarios to plan tactics, techniques, and procedures (TTPs) with contingencies as operations unfold. However, they are extremely vulnerable to human biases and omissions that undermine the assessment of evidence, statistical analysis, and the understanding of cause and effect.

Third, generative AI methods are being integrated into the operational planning process and can enrich the development of a range of ISRT strategies. However, it must start all over again if "Unknown-Unknown" events crash the ongoing TTPs.

This SBIR topic will develop Observant-AI agents as a class of regenerative AI that learn in real time, enables active visual and tactical monitoring of anomalous activities, and trigger I&W alerts in naval operations. The goal of the effort is to perform a combination of offline and online predictive engagement modeling to plan for trusted AI-enabled TTPs that will strategically adjust plans in real time to adapt to emerging events and conditions.

Critical AI technology components and developments include: Contextual modeling; Multidomain multimodal all-source intelligence data and signals; Data learning; Data quality, data interoperability, data generation; Data storage; Spatiotemporal synchronization methods; Multimodal contextual signal processing and fusion; Cross-domain contextual collaborative learning, inference, and recognition; Contextual collaboration, adaptation, and teaming via ad-hoc networking; Contextual reasoning, risk assessment, and risk reduction; Contextual query, question-answering (Q&A), and natural language processing; Contextual priority-based task management; AI-risk escalation control methods; AI-assisted targeteer maneuvers and engagements.

Work produced in Phase II may become classified.

PHASE I: Determine the technical feasibility of designing and developing the Observant-AI technology described in the Description section. Draw key distinctions for the proposed design approach compared to the current state-of-the-art naval ISRT information exploitation systems. Motivate the design with three compelling challenge problems supported by relevant datasets. Consider challenge problems corresponding to cross-domain littoral operations and navigational risks countering anti-access/denied-access enforcement scenarios. Conduct end-to-end Observant-AI system performance assessment. Deliverables include end-to-end initial prototype technology, T&E, demonstration, a plan for Phase II, and a final report.

PHASE II: Conduct proof-of-concept and prototype development incorporating the recommended candidate technology from Phase I. Test and demonstrate the improved capability based on the performance metrics detailed for Phase I with requirements: Analytic Completeness < 98%, Uniqueness < 98%, Validity < 98%, Consistency < 98%, and Accuracy < 98%. Provide the following deliverables: analytics, signal processing tools, models, prototypes, T&E and demonstration results, interface requirements, and final report. It is probable that the work under this effort will be classified under Phase II.

PHASE III DUAL USE APPLICATIONS: Advance these capabilities to TRL-7 and integrate the technology into the Maritime Tactical Command and Control POR, Marine Air-Ground Task Force Command and Control, or ISR processing platforms at the Marine Corps Information Operations Center. Once conceptually and technically validated, demonstrate dual-use applications of this technology in civilian law enforcement and security services.

KEYWORDS: Risk-Aware, Regenerative, Artificial Intelligence, Machine Learning, Contextual, Multimodal, Cross-Domain, Visual-Tactical ISRT