Using Machine Learning to Increase NPC Fidelity with Dynamic Preferences Used in Forward Looking Decisions

Abstract

This report summarizes our experience in modeling decision-making preferences within a distributed system of simulated users for cybersecurity training and exercise1. It also provides detail of our explorations of machine learning solutions and how we ultimately enabled increasingly life-like computer activity to autonomous agents over time. In addition, the particular problem we discuss is well-suited to others looking to build an introductory implementation of machine learning, in that our solution requires neither significant investment of money, resources, or data. We hope that operations teams will consider the problems they encounter in the same spirit as our approach, whereby the solution to the problem continues to improve itself in an automated fashion over time.

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Document Details

Document Type
Technical Report
Publication Date
Feb 18, 2021
Accession Number
AD1126910

Entities

People

  • Dustin D. Updyke
  • Geoffrey B. Dobson
  • Thomas G. Podnar

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Cyber

DTIC Thesaurus Topics

  • Computer Networks
  • Computers
  • Computing Devices
  • Cyber Warfare
  • Cybersecurity
  • Cyberwarfare
  • Department Of Defense
  • Education
  • Human Behavior
  • Internet
  • Machine Learning
  • Marine Corps
  • Materials
  • Networks
  • Recreation
  • Reliability
  • Simulations
  • Social Environment
  • Social Media
  • Social Networking Services
  • Software Development
  • United States
  • Websites

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Economics
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Cyber