Autonomous Metacognitive Collection Orchestration for Multi-Domain Intelligence

Develop and demonstrate an autonomous, metacognitive artificial-intelligence capability that orchestrates multi-modal intelligence collection across asynchronous sensing streams and produces traceable, causally-informed recommendations for multi-domain operations. The capability shall decide what evidence to collect, when, and why; weigh expected information gain against hard operational constraints including emissions, bandwidth, power, time-to-decision, and access posture; generate Pareto-optimal option-sets accompanied by “why / why-not” traces reproducible from internal system state; and locally refine its internal representation under denial, deception, or distribution shift without offline retraining. Operator interaction is confined to pre-mission intent specification and after-action audit of the decision record, enabling a human-machine formation in which the capability operates at machine speed inside a commander-specified envelope and the human sets intent, constraints, and rules of engagement at mission entry and reviews the auditable decision record at mission exit.

The full objective, description, Phase I, II and III requirements and evaluation criteria are in the official topic on DSIP.