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C2 processes are very different from many of the games and environments used to develop and demonstrate AI systems

• Game-playing algorithms exploit regularity to achieve superhuman performance, but nature and the adversary intervene to break this simplifying assumption in military tasks.
• Characterizing and developing representative problems and environments will enable research, development, testing, and evaluation of AI systems under conditions representative of DoD problem sets, thereby increasing transferability to operational environments.

The distinctive nature of C2 processes calls for AI systems different from those optimized for game play

• Understanding the capabilities and limitations of existing AI systems will allow the Air Force to identify systems that are suitable for different C2 processes.
• Choosing the right approach at problem outset can substantially reduce application development time, increase solution quality, and decrease risk associated with transitioning the solution.

New guidance, infrastructure, and metrics are needed to evaluate applications of AI to C2

• Establishing and operationalizing measures of merit will enable the evaluation and comparison of potential AI systems.
• Additionally, measures of merit provide a way to communicate the return on investment of AI-enabled C2.

Hybrid approaches are needed to deal with the multitude of problem characteristics that are present in C2 processes

• Given the generality of the analytical framework and the emergence of Joint All-Domain C2, all these conclusions and recommendations extend to the pursuit of AI across DoD.

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.

• Chapter One

  Introduction and Project Overview

• Chapter Two

  Taxonomy of Problem Characteristics

• Chapter Three

  Taxonomy of Solution Capabilities

• Chapter Four

  Mapping Problem Characteristics to Solution Capabilities

• Chapter Five

  Metrics for Evaluating Artificial Intelligence Solutions

• Chapter Six

  Conclusion and Recommendations