Leveraging Machine Learning for Operation Assessment

Abstract

The authors describe an approach for leveraging machine learning to support assessment of military operations. They demonstrate how machine learning can be used to rapidly and systematically extract assessment-relevant insights from unstructured text available in intelligence reporting, operational reporting, and traditional and social media. These data, already collected by operational-level headquarters, are often the best available source of information about the local population and enemy and partner forces but are rarely included in assessment because they are not structured in a way that is easily amenable to analysis. The machine learning approach described in this report helps overcome this challenge.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2022
Accession Number
AD1168242

Entities

People

  • Daniel Egel
  • Jasmin Leveille
  • Linda Robinson
  • Luke J. Matthews
  • Mary K. Adgie
  • Ryan A. Brown

Organizations

  • RAND Corporation

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Engineered Resilient Systems
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Automata Theory
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Computer Programs
  • Data Analysis
  • Data Curation
  • Data Mining
  • Information Science
  • Kernel Functions
  • Military Science
  • Network Science
  • Neural Networks
  • Social Media
  • Supervised Machine Learning
  • War Colleges

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Joint Military Operations and Doctrine.
  • Systems Analysis and Design

Technology Areas

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