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22 April 2020 AI-enabled wargaming in the military decision making process
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Abstract
During the Course of Action (COA) Analysis stage of the Military Decision Making Process (MDMP), staff members wargame the options of both friendly and enemy forces in an action-reaction-counteraction cycle to expose and address potential issues. This is currently a manual, subjective process, so many assumptions often go untested and only a very small number of alternative COAs may be considered. The final COA that is produced might miss opportunities or overlook risks. This challenge will only be exacerbated during Multi-Domain Operations (MDO), in which larger numbers of entities are expected to coordinate across domains to achieve converged effects within compressed timelines. This paper describes a prototype wargaming software support tool that leverages Artificial Intelligence (AI) to recommend COA improvements to commanders and staff. The tool’s design accounts for operational realities including a lack of available AI training data, limited tactical computing resources, and a need for end user interaction throughout the COA Analysis process. Given initial COAs for friendly and enemy forces, the tool searches for improvements by repeatedly proposing changes to the friendly COA and running the Data Analysis and Visualization INfrastructure for C4ISR (DAVINCI) combat simulation to evaluate them. Runtime is managed by carefully restricting the search space of the AI to only consider doctrinally relevant changes to the COA. The system architecture is designed to separate the AI, the simulation, and the user interface, simplifying continued experimentation and enhancements. The design of the AIenabled wargaming tool is presented along with initial results.
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Peter J. Schwartz, Daniel V. O'Neill, Meghan E. Bentz, Adam Brown, Brian S. Doyle, Olivia C. Liepa, Robert Lawrence, and Richard D. Hull "AI-enabled wargaming in the military decision making process", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114130H (22 April 2020); https://doi.org/10.1117/12.2560494
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