Exploring the Feasibility and Utility of Machine Learning-Assisted Command and Control
Jul 15, 2021
This report, which describes the potential for artificial intelligence (AI) systems to assist in Air Force command and control (C2) from a technical perspective, presents an analytical framework for assessing the suitability of a given AI system for a given C2 problem. The authors also provide metrics that can be used to evaluate AI systems and to demonstrate and socialize their utility.
Volume 1, Findings and Recommendations
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This report concerns the potential for artificial intelligence (AI) systems to assist in Air Force command and control (C2) from a technical perspective. The authors present an analytical framework for assessing the suitability of a given AI system for a given C2 problem. The purpose of the framework is to identify AI systems that address the distinct needs of different C2 problems and to identify the technical gaps that remain. Although the authors focus on C2, the analytical framework applies to other warfighting functions and services as well.
The goal of C2 is to enable what is operationally possible by planning, synchronizing, and integrating forces in time and purpose. The authors first present a taxonomy of problem characteristics and apply them to numerous games and C2 processes. Recent commercial applications of AI systems underscore that AI offers real-world value and can function successfully as components of larger human-machine teams. The authors outline a taxonomy of solution capabilities and apply them to numerous AI systems.
While primarily focusing on determining alignment between AI systems and C2 processes, the report's analysis of C2 processes is also informative with respect to pervasive technological capabilities that will be required of Department of Defense (DoD) AI systems. Finally, the authors develop metrics — based on measures of performance, effectiveness, and suitability — that can be used to evaluate AI systems, once implemented, and to demonstrate and socialize their utility.
Introduction and Project Overview
Taxonomy of Problem Characteristics
Taxonomy of Solution Capabilities
Mapping Problem Characteristics to Solution Capabilities
Metrics for Evaluating Artificial Intelligence Solutions
Conclusion and Recommendations
The research described in this report was prepared for the the Air Force Research Laboratory, Information Directorate (AFRL/RI) and conducted by the Force Modernization and Employment Program within RAND Project AIR FORCE.
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