Modern military operations are characterized by an astronomically large strategy space, where adversaries’ actions are interdependent. Current planning processes are human-intensive, slow, and explore only a "vanishingly small fraction" of possible COAs for both Blue and Red forces. This creates significant operational risk and leaves unexploited opportunities on the table. Standard machine learning approaches are often insufficient as they require massive, labeled datasets that do not exist for future conflicts and frequently produce "black box" solutions that are difficult for commanders to trust, interpret, or certify. This topic seeks solutions founded in computational game theory capable of computing approximate Nash equilibria in large-scale, zero-sum, imperfect-information games. The desired AI engine will use self-play within high-fidelity simulation environments to learn and refine strategies for both Blue and Red sides simultaneously, without requiring a priori assumptions about adversary tactics. The proposed solution must demonstrate the following critical attributes: 1. Dominant Performance: The system must generate COAs that are demonstrably superior to those developed by expert human planners in complex military scenarios. The ability to defeat experienced red teams is the paramount evaluation criterion. 2. Human-Interpretability: Generated strategies must be transparent and understandable, composed of modular, doctrinally-relevant planning components (i.e., not a monolithic neural network). Commanders must be able to understand the "why" behind the AI's recommendations. 3. Scalability: The AI architecture must be capable of scaling from tactical engagements (e.g., individual flight combat) to operational-level scenarios involving thousands of assets across multiple domains (air, sea, land) and extended time horizons. 4. Computational Efficiency: The solution should operate effectively on modest computational footprints (e.g., single or small-cluster CPU-based workstations), avoiding reliance on cost-prohibitive, large-scale GPU clusters for its core training and inference loops. 5. "Anytime" Capability: The algorithm must be capable of providing a valid, usable strategy at any point during its computation cycle, with the solution quality improving as more time and resources are allocated. FEASIBILITY DOCUMENTATION: • Documentation should include all relevant information including, but not limited to: technical reports, test data, prototype designs/models, and performance goals/results.
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