Standoff Sensing of Electronic Systems

Abstract

We consider the problem of multi-task reinforcement learning (MTRL) in multiple partially observable stochastic environments. This model is appropriate for the statistical analysis of electronic systems via standoff sensing. The electronic circuit is partially observable. The framework permits "life-long" learning, in which the algorithm continually improves with time, as it sees more environments and systems.

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Document Details

Document Type
Technical Report
Publication Date
Mar 12, 2011
Accession Number
ADA545007

Entities

People

  • Lawrence Carin

Organizations

  • Duke University

Tags

Communities of Interest

  • Autonomy
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Bayesian Networks
  • Computational Science
  • Gaussian Distributions
  • Hidden Markov Models
  • Information Processing
  • Information Retrieval
  • Information Science
  • Information Systems
  • Machine Learning
  • Markov Models
  • Monte Carlo Method
  • Neural Networks
  • Operations Research
  • Probabilistic Models
  • Probability
  • Probability Distributions

Fields of Study

  • Computer science

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Microelectronics