Machine Learning for Operational Decisionmaking in Competition and Conflict: A Demonstration Using the Conflict in Eastern Ukraine

Abstract

The integration of machine learning into military decision making is widely seen as critical for the United States to retain its military dominance in the21st century. Advances in machine learning have the potential to dramatically change the character of warfare by enhancing the speed, precision, and efficacy of decision making across the national security enterprise. Leaders across the U.S. Department of Defense recognize this, and a multitude of efforts are underway to effectively integrate machine learning tools across the tactical, operational, strategic, and institutional levels of war.

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Document Details

Document Type
Technical Report
Publication Date
Oct 02, 2023
Accession Number
AD1211977

Entities

People

  • Daniel Egel
  • Eric Robinson
  • George Bailey

Organizations

  • RAND Corporation

Tags

Communities of Interest

  • Autonomy
  • C4I

DTIC Thesaurus Topics

  • Aircrafts
  • Artificial Intelligence
  • Case Studies
  • Command And Control
  • Computer Programming
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Databases
  • Department Of Defense
  • Drone Targeting
  • Governments
  • Human-Machine Systems
  • Information Science
  • Machine Learning
  • Military Applications
  • National Security
  • Operations Research
  • Public Policy
  • Supervised Machine Learning
  • United States
  • United States European Command
  • Warfare

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Enterprise Information Systems Architecture and Joint Command Capability Interoperability Support.
  • Military History / Militaries and War Studies

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks