Monte Carlo Tree Search Applied to a Modified Pursuit/Evasion Scotland Yard Game with Rendezvous Spaceflight Operation Applications

Abstract

Space has become a warfighting domain. To combat threats, an understanding of tactics, techniques, and procedures must be captured and studied. Games and simulations are effective tools to capture data lacking historical context. Artificial intelligence models use simulations to develop proper defensive and offensive TTPs capable of protecting systems against potential threats. Monte Carlo Tree Search is a bandit-based reinforcement learning model known for using limited domain knowledge to push favorable results.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2020
Accession Number
AD1104463

Entities

People

  • Joshua A Daugherty

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Artificial Satellites
  • Data Mining
  • Deep Learning
  • Environment
  • Game Theory
  • Information Science
  • Machine Learning
  • Reinforcement Learning
  • Simulations
  • Space Objects
  • Space Systems
  • Spacecraft
  • Three Dimensional

Readers

  • Irregular Warfare and Special Operations Cyberspace Operations against Adversarial Threats.
  • Neural Network Machine Learning.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • Space