Digital Engineering Enhanced T and E of Learning-Based Systems

Abstract

The current approach to Test and Evaluation (T and E) involves treating the system in a blackbox fashion, i.e., the system is presented with sample inputs, and the corresponding outputs are observed and characterized relative to expectations. While such an approach works well for traditional static systems, test and evaluation of autonomous intelligent systems presents formidable challenges due to the dynamic environments of the agents, adaptive learning behaviors of individual agents, complex interactions between agents and the operational environment, difficulty in testing black box machine learning (ML) models, and rapidly evolving ML models and AI algorithms [1, 2].

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Document Details

Document Type
Technical Report
Publication Date
Jun 21, 2022
Accession Number
AD1176657

Entities

People

  • Jitesh Panchal
  • Laura Freeman
  • Peter A. Beling

Organizations

  • Virginia Tech

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Cyber
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Acquisition
  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • Computational Science
  • Computer Vision
  • Detection
  • Digital Engineering
  • Engineering
  • Hierarchies
  • Machine Learning
  • Mathematical Models
  • Neural Networks
  • Probability
  • Probability Distributions
  • Reliability
  • Systems Engineering
  • Test And Evaluation
  • Unmanned Aerial Vehicles

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

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