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Edward Geist

    Exploring the Feasibility and Utility of Machine Learning-Assisted Command and Control, Volume 1
    • This report examines the potential of artificial intelligence (AI) systems to enhance Air Force command and control (C2) from a technical standpoint. The authors introduce an analytical framework designed to evaluate the suitability of specific AI systems for various C2 challenges, aiming to pinpoint those that meet the unique demands of different C2 scenarios while identifying existing technical gaps. Although the primary focus is on C2, the framework is applicable to other warfighting functions and services. C2's objective is to facilitate operational possibilities by effectively planning, synchronizing, and integrating forces. The authors provide a taxonomy of problem characteristics and apply it to various games and C2 processes. Recent commercial AI applications demonstrate that AI can deliver tangible value and operate effectively as part of larger human-machine teams. They also outline a taxonomy of solution capabilities relevant to numerous AI systems. While the report mainly emphasizes aligning AI systems with C2 processes, its analysis also sheds light on the technological capabilities essential for Department of Defense (DoD) AI systems. Lastly, the authors propose metrics based on performance, effectiveness, and suitability to evaluate AI systems post-implementation and illustrate their utility.

      Exploring the Feasibility and Utility of Machine Learning-Assisted Command and Control, Volume 1