Streamlined AI architecture for wargaming

Melanie A. Rose; Shaun M. Ryer

doi:10.1117/12.3012668

7 June 2024 Streamlined AI architecture for wargaming

Melanie A. Rose, Shaun M. Ryer

Proceedings Volume 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI; 130510S (2024) https://doi.org/10.1117/12.3012668
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Legatus AI, an Air Force program supporting joint and coalition partners, is addressing the need for the creation of a common architecture of software and hardware for campaign level planning to empower the efficient development of AI and core AI capabilities. Building off lessons learned in previous command and control programs, Legatus AI seeks to close the capability gap by transforming AI development from non-reusable boutique solutions to an ecosystem of modular and reusable processes for configuring, training, evaluating, and deploying reinforcement learning and game theory agents for planning and wargaming environments. The prototype architecture added reinforcement learning capabilities to StreamlinedML to allow for communication between AI agents and games using a Docker-based solution. The architecture was tested on the Airlift Challenge simulation to ensure intended functionality and demonstrate the initial capability. Future work will incorporate additional agents and Unity-based wargames. The maturation of this architecture is a key cornerstone for future collaborative research and development of adaptable AI and plug-and-play wargaming platforms.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Melanie A. Rose and Shaun M. Ryer "Streamlined AI architecture for wargaming", Proc. SPIE 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, 130510S (7 June 2024); https://doi.org/10.1117/12.3012668

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