Artificial Intelligence and Machine Learning Applications for Defensive Counterspace

A Decision Support Tool Capability Demonstration

George Nacouzi, Osonde A. Osoba, Jonathan Tran, Sascha Ishikawa

ResearchPublished Jan 11, 2024

This report summarizes results from a fiscal year 2020 project examining the applicability of artificial intelligence and machine learning as an enabler of decision support in defensive counterspace missions. This work includes a decision support tool to demonstrate the viability of the suggested algorithm and provide insight into the value of such a tool. Proposed improvements to the approach are also provided.

Topics

Document Details

Citation

RAND Style Manual
Nacouzi, George, Osonde A. Osoba, Jonathan Tran, and Sascha Ishikawa, Artificial Intelligence and Machine Learning Applications for Defensive Counterspace: A Decision Support Tool Capability Demonstration, RAND Corporation, RR-A582-1, 2024. As of October 10, 2024: https://www.rand.org/pubs/research_reports/RRA582-1.html
Chicago Manual of Style
Nacouzi, George, Osonde A. Osoba, Jonathan Tran, and Sascha Ishikawa, Artificial Intelligence and Machine Learning Applications for Defensive Counterspace: A Decision Support Tool Capability Demonstration. Santa Monica, CA: RAND Corporation, 2024. https://www.rand.org/pubs/research_reports/RRA582-1.html.
BibTeX RIS

Research conducted by

The research reported here was commissioned by the Department of the Air Force's United States Space Force and conducted within the Force Modernization and Employment Program of RAND Project AIR FORCE.

This publication is part of the RAND research report series. Research reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND research reports undergo rigorous peer review to ensure high standards for research quality and objectivity.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.