Hyperdimensional Neurosymbolic Cyber Course-of-Action Planning and Replanning under Deception

SBIR topic DPA27BZ02-DV001 (U.S. Department of Defense – DARPA), Phase I and II. Open to U.S. small businesses. Opens: December 2, 2026. Closes: January 6, 2027. Future cyber operations will be conducted in contested environments in which observations are incomplete, timing is compressed, adversaries actively manipulate defender beliefs, and the state of the mission cannot be fully observed from any single node or operator position. In such settings, success depends not only on detecting malicious behavior, but on understanding what actions remain feasible, what dependencies govern those actions, which alternatives best satisfy mission objectives, and how plans should be revised as assumptions fail or new opportunities emerge. Current cyber tools do not adequately support this planning problem. Many existing capabilities focus on alerting, anomaly scoring, static attack-path enumeration, or post hoc analytics. These functions are valuable, but they do not provide operators with an integrated capability to construct, compare, and revise cyber COAs as the environment changes. Rule-based playbooks can be brittle under novelty and obfuscation. Optimization and reinforcement-learning approaches may generate recommendations but often provide limited interpretability and can be difficult to adapt when observations are sparse, manipulated, or only partially relevant. Large-model assistants may accelerate interaction, but by themselves do not provide a persistent, auditable, machine-interpretable planning state suitable for operational use. This topic seeks a new class of cyber planning technologies built around hyperdimensional, vector-symbolic, neurosymbolic, or closely related compositional reasoning methods. Of particular interest are approaches that maintain explicit internal representations of cyber objectives, prerequisites, access dependencies, branching opportunities, constraints, anticipated adversary reactions, and uncertainty in forms that are robust,…