Decentralized Adaptive Cognitive TTP Synthesis (DACTS)

GBL Systems proposes the Decentralized Adaptive Cognitive Tactics, Techniques, and Procedures (TTP) Synthesis (DACTS) project to synthesize machine-actionable TTPs utilizing supervised/unsupervised AI/ML methods. DACTS provides decision logic for self-coordinating tactical systems including low-SWaP platforms in operationally-relevant environments (e.g., A2/AD). DACTS will update a threat model in real time utilizing unsupervised ML methods which are utilized to synthesize/update TTPs to counter projected threat behavior. AI/ML methodology will provide appropriate computation speed for real-time reactions and projected task timelines. This allows heterogeneous EW platforms (including manned and unmanned systems) to develop real-time many-versus-many task plans that adapt to adversary warfighting systems (e.g., Integrated Air Defense Systems). The heterogeneous platforms in this use case will have varied ES and EA capabilities and coordinate with peers in a decentralized manner to de-conflict tasking and support dependent EW activities. Phase I will identify key requirements and AI/ML algorithms (including new research on unsupervised, supervised, and reinforcement learning) as well as refine a system architecture, and a Phase I Option will prototype advanced system components for use in the classified Phase II development process. To achieve success and reduce development risk, GBL will leverage experience and flight-tested technology from prior SBIR efforts and Independent Research and Development.