Assured Autonomy Using Dynamic Monitors and Simulation (ADAM DMS)

Abstract

Systems that are forced out of the certified operating configuration can, and do, fail catastrophically. ln many cases, the systems can be saved by executing often counter intuitive actions. With a complex system, it is unlikely that a human operator would be able to "discover" these recovery actions before the system is destroyed. Using a high precision simulator, machine learning enables the research to learn action policies that can save a system from catastrophic failure, if it is caught quickly enough. The Assured Autonomy using Dynamic Monitors and Simulation (ADAM DMS) system learns the safe operating configuration and deep learning allows synthesis of a monitor and the learned recovery policy can be synthesized to perform the recovery. A simulated quadcopter was used as a demonstration example.

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Document Details

Document Type
Technical Report
Publication Date
May 17, 2022
Accession Number
AD1169027

Entities

People

  • Paul Robertson
  • Prakash Manghwani

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • C4I
  • Cyber
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Air Force Research Laboratories
  • Aircrafts
  • Algorithms
  • Classification
  • Computational Fluid Dynamics
  • Computational Science
  • Data Sets
  • Differential Equations
  • Equations
  • Failure Mode And Effect Analysis
  • Graphics Processing Unit
  • Information Science
  • Machine Learning
  • Neural Networks
  • Probability
  • Supervised Machine Learning
  • Two Dimensional
  • Unmanned Aerial Vehicles

Fields of Study

  • Computer science

Readers

  • Educational Psychology
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems