Report
Exploring the Feasibility and Utility of Machine Learning-Assisted Command and Control
Jul 15, 2021
Volume 2, Supporting Technical Analysis
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This volume serves as the technical analysis to a report concerning the potential for artificial intelligence (AI) systems to assist in Air Force command and control (C2) from a technical perspective. The authors detail the taxonomy of ten C2 problem characteristics. They present the results of a structured interview protocol that enabled scoring of problem characteristics for C2 processes with subject-matter experts (SMEs). Using the problem taxonomy and the structured interview protocol, they analyzed ten games and ten C2 processes. To demonstrate the problem taxonomy and the structured interview protocol for a C2 problem, they then applied them to sensor management as performed by an air battle manager.
The authors then turn to eight AI system solution capabilities. As for the C2 problem characteristics, they created a structured protocol to enable valid and reliable scoring of solution capabilities for a given AI system. Using the solution taxonomy and the structured interview protocol, they analyzed ten AI systems.
The authors present additional details about the design, implementation, and results of the expert panel that was used to determine which of the eight solution capabilities are needed to address each of the ten problem characteristics. Finally, they present three technical case studies that demonstrate a wide range of computational, AI, and human solutions to various C2 problems.
Chapter One
Analysis of Problem Characteristics
Chapter Two
Analysis of Solution Capabilities
Chapter Three
Expert Panel Design, Implementation, and Additional Results
Chapter Four
Metrics for Evaluating Artificial Intelligence Solutions
Chapter Five
Case Study 1: Master Air Attack Planning
Chapter Six
Case Study 2: Automatic Target Recognition with Learning
Chapter Seven
Case Study 3: Human-Machine Teaming for Personnel Recovery
Appendix A
Artificial Intelligence History
Appendix B
Mathematical Details for Closed-Loop Automatic Target Recognition
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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