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, updateable, and inspectable by both machines and operators. Hyperdimensional and related high-dimensional symbolic representations are of interest when they enable compact encoding of structured cyber context; efficient binding of entities, roles, temporal order, and dependencies; similarity-based retrieval across partially matching situations; graceful degradation under incomplete or deceptive observations; and deployment in resource-constrained or communications-constrained environments where retraining-heavy or centralized approaches are poor fits.
This topic seeks prototype capabilities that support cyber COA planning and replanning as structured decision processes rather than static checklists. Desired systems should represent plans through inspectable mission graphs, task webs, tiled workspaces, or comparable compositional artifacts in which each element captures sub-objectives, preconditions, dependencies, candidate actions, supporting evidence, uncertainty, timing, cost, reversibility, and mission relevance. The system should generate multiple feasible COAs for a stated objective, compare alternatives using explicit decision criteria, identify key assumptions and information gaps, and recommend revisions when new evidence changes branch viability or reveals newly feasible options.
A responsive solution should not treat hyperdimensional or neurosymbolic reasoning as novelty for its own sake. Instead, the proposer should clearly show how the chosen representation materially improves cyber planning performance in one or more of the following ways:
• compact encoding of evolving cyber context and plan structure without exhaustive recomputation;
• compositional updates as new observations, constraints, or objectives arrive;
• retrieval of relevant prior plan fragments or analogous situations under partial mismatch;
• maintenance of competing plan branches or hypotheses under obfuscation and ambiguity;
• lightweight exchange of decision-relevant symbolic or semantic representation among distributed agents or operator cells;
• interpretable rationale linking recommended actions to plan structure, assumptions, and uncertainty.
Representative operational settings of interest may include distributed mission networks, cyber support to software-reliant tactical platforms, planning for AI-enabled mission systems whose data or control pathways may be degraded or manipulated, or collaborative cyber planning cells operating with intermittent connectivity and incomplete common operating pictures. In these settings, no single operator or software agent may possess the entire relevant state. Accordingly, solutions should support selective sharing of compact, decision-relevant abstractions rather than requiring full-state synchronization, centralized fusion, or cloud-dependent reasoning.
The topic is especially interested in technologies that support one or more of the following planning functions:
• representation of cyber objectives, constraints, prerequisites, opportunities, and dependencies in machine-reasonable structured form;
• generation of multiple feasible COAs rather than a single opaque recommendation;
• comparison and ranking of alternative COAs using explicit criteria such as mission value, exposure, time-to-effect, reversibility, resource burden, and uncertainty;
• rapid replanning when observations are incomplete, delayed, deceptive, or contradictory, or when mission priorities change;
• distributed coordination through compact semantic or symbolic representation appropriate for degraded communications;
• operator-facing explanation that identifies why a plan is recommended, what assumptions drive it, what alternatives remain available, and what additional information would most improve the decision.
This topic is not seeking generic dashboards, static attack-graph visualizers, anomaly detectors, signature-only defenses, or chatbot-style assistants that provide recommendations without persistent structured state and auditable reasoning. Likewise, the topic is not primarily seeking exploit-generation tools, malware development, or automated offensive cyber execution. Proposed efforts should remain centered on interpretable planning and replanning support for cyber decision-making in contested environments.
Proposals should clearly define the operational user, the planning loop being supported, the boundaries of autonomy, and the expected decision advantage over current practice. Proposals should also explain why the chosen hyperdimensional, neurosymbolic, or related representation is superior to conventional rule tables, static graph models, retraining-heavy black-box approaches, or exhaustive search in the proposer’s target setting.
Relevant evaluation measures may include, but are not limited to:
time to first viable COA;
• quality and diversity of top-k recommended COAs;
• calibration of predicted plan utility or mission success likelihood;
• replanning latency after injected contradictory, incomplete, or deceptive evidence;
• robustness to novel tactics, techniques, and procedures or unseen mission contexts;
• bandwidth and compute burden for distributed coordination;
• memory, latency, and power performance on representative constrained hardware;
• operator comprehension, trust, workload, and explanation usefulness;
• performance relative to appropriate baseline approaches such as rule-based playbooks, static graph methods, optimization-only planners, reinforcement learning without explicit symbolic structure, or large-language-model-based planning assistants without grounded internal state.
