Game-Theoretic AI for Robust Course of Action (COA) Generation and Wargaming

Game-Theoretic AI for Robust Course of Action (COA) Generation and Wargaming is sponsored by Office of the Secretary of Defense (DoD). This SBIR/STTR topic seeks to develop and demonstrate a mature, scalable, and robust game-theoretic Artificial Intelligence (AI) engine capable of generating and executing novel, optimized courses of action (COAs) in complex, multi-domain, imperfect-information wargaming environ…

Automated Course of Action Generation – Army SBIR|STTR Program Artificial Intelligence/Machine Learning, Army SBIR, Phase I Automated Course of Action Generation Application Due Date: 01/08/2025 Leverage state of the art AI and ML to potentially automate Course of Action (CoA) recommendations and improve our Military Decision-Making Process (MDMP) at the tactical level.

Leveraging state-of-the-art Artificial Intelligence/Machine Learning (AI/ML) will speed up this process by an order of magnitude, allowing systematic replanning during the execution phase of operations. This will improve mission success and reduce risk to force in combat operations. Several approaches to CoA generation for military applications currently exist.

These methods leverage large language models (LLM) applied to Doctrine or deep learning/hierarchical modeling applied to wargaming. Each existing LLM approach is inadequate for CoA generation at the tactical level due to lack of proper consideration of Tactics, Techniques and Procedures (TTPs) and Standard Operating Procedures (SOPs) for both friendly and enemy forces.

The existing learned/hierarchical approaches that depend on wargaming are also inadequate due to a lack of fully automated wargaming capability at the tactical level and/or due to CoA outcomes that are not aligned with best practices (TTPs and SOPs). Proposals submitted under this topic will leverage state-of-the-art Artificial Intelligence (AI) approaches to create a CoA recommendation module narrowly tailored to the tactical level.

This capability will expand to encompass higher echelons in later phases. This topic is only accepting Phase I proposals for a cost up to $250,000 for a 6-month period of performance. Understand relevant scenarios and data sources.

Design development strategy. Conduct feasibility study. Develop and demonstrate proof-of-concept CoA recommendation algorithm.

Develop and train CoA recommendation agent and support integration into appropriate mission command system. Make interim algorithm deliveries biannually for independent evaluation. Deliver final algorithm, supporting software, documentation and algorithm performance report.

Automated CoA generation has wide applicability to autonomous systems in industries such as robotics, self-driving automobiles, security, and gaming. Financial Services & Personal Finance: Aides in professional, financial services planning and “gamming” out as well as planning for things like retirement and estates.

Retail and Supply Chain: These models can help ensure that supply chains aren’t strained for retail products (can also work for B2B solutions). Construction & Manufacturing: AI models can assist in project planning and forecast unforeseen problems Healthcare: Aides in personal healthcare forecasting and preventative medicine. For more information, and to submit your full proposal package, visit the DSIP Portal .

SBIR|STTR Help Desk: usarmy. sbirsttr@army. mil https://deepmind.

google/technologies/alphazero-and-muzero/ https://deepmind. google/discover/blog/alphastar-mastering-the-real-time-strategy-game-starcraft-ii/ https://openai.

com/index/emergent-tool-use/ KEYWORDS: Autonomy; AI/ML; Course of Action; Command and Control; Multi-agent Leverage state of the art AI and ML to potentially automate Course of Action (CoA) recommendations and improve our Military Decision-Making Process (MDMP) at the tactical level.

Leveraging state-of-the-art Artificial Intelligence/Machine Learning (AI/ML) will speed up this process by an order of magnitude, allowing systematic replanning during the execution phase of operations. This will improve mission success and reduce risk to force in combat operations. Several approaches to CoA generation for military applications currently exist.

These methods leverage large language models (LLM) applied to Doctrine or deep learning/hierarchical modeling applied to wargaming. Each existing LLM approach is inadequate for CoA generation at the tactical level due to lack of proper consideration of Tactics, Techniques and Procedures (TTPs) and Standard Operating Procedures (SOPs) for both friendly and enemy forces.

The existing learned/hierarchical approaches that depend on wargaming are also inadequate due to a lack of fully automated wargaming capability at the tactical level and/or due to CoA outcomes that are not aligned with best practices (TTPs and SOPs). Proposals submitted under this topic will leverage state-of-the-art Artificial Intelligence (AI) approaches to create a CoA recommendation module narrowly tailored to the tactical level.

This capability will expand to encompass higher echelons in later phases. This topic is only accepting Phase I proposals for a cost up to $250,000 for a 6-month period of performance. Understand relevant scenarios and data sources.

Design development strategy. Conduct feasibility study. Develop and demonstrate proof-of-concept CoA recommendation algorithm.

Develop and train CoA recommendation agent and support integration into appropriate mission command system. Make interim algorithm deliveries biannually for independent evaluation. Deliver final algorithm, supporting software, documentation and algorithm performance report.

Automated CoA generation has wide applicability to autonomous systems in industries such as robotics, self-driving automobiles, security, and gaming. Financial Services & Personal Finance: Aides in professional, financial services planning and “gamming” out as well as planning for things like retirement and estates.

Retail and Supply Chain: These models can help ensure that supply chains aren’t strained for retail products (can also work for B2B solutions). Construction & Manufacturing: AI models can assist in project planning and forecast unforeseen problems Healthcare: Aides in personal healthcare forecasting and preventative medicine. For more information, and to submit your full proposal package, visit the DSIP Portal .

SBIR|STTR Help Desk: usarmy. sbirsttr@army. mil https://deepmind.

google/technologies/alphazero-and-muzero/ https://deepmind. google/discover/blog/alphastar-mastering-the-real-time-strategy-game-starcraft-ii/ https://openai.

com/index/emergent-tool-use/ KEYWORDS: Autonomy; AI/ML; Course of Action; Command and Control; Multi-agent Assistant Secretary of the Army for Acquisition, Logistics, and Technology ASA(ALT) releases contract opportunities on an ad-hoc basis to meet Army research and development needs.

Army Futures Command (AFC) releases topics during three specific solicitation periods throughout the fiscal year to address the Army’s current and anticipated war-fighting technology needs. Army STTR follows AFC’s topic release schedule but partners with a university, federally funded research and development center, or a qualified non-profit research institution as part of their contract.

Is the opportunity to establish the scientific, technical, commercial merit and feasibility of your proposed innovation. Is focused on the development, demonstration and delivery of your innovation from Phase I. Represents the commercialization phase of the program in which the company can market their products or services developed in Phase II, either to the government or in the commercial sector.

Allows small businesses to submit to Direct to Phase II applications if they performed the Phase I research through other funding sources. Provides funding to projects that require additional funding during their open Phase II contract. A Phase II Awardee may receive one additional, sequential Phase II award to continue the work of an initial Phase II award.

The sequential Phase II award has the same guideline amounts and limits as an initial Phase II award.

Artificial Intelligence/Machine Learning (supply chain management, logistics coordination, target identifications and simulation) Advanced Materials and Manufacturing (additive manufacturing) Autonomy (unmanned systems, drones, ground vehicle capabilities) Chemical and Biological (detection, defense) Cyber (biometric authentication, secure communications) Electronics (microelectronics, Very-Large-Scale Integration (VLSI)) Electronic Warfare (jamming, spoofing) Human Performance (wearables) Immersive (augmented reality, virtual reality, mixed reality) Network Technologies (antennas, radio frequency, communications systems) Position, Navigation, and Timing (GPS) Power (batteries, generators) Software Modernization (high performance computing, data management and visualization) Sensors (infrared sensing) Weapons Systems (hypersonics, munitions and projectiles, directed energy)