Assurance for Learning-Enabled Systems (ALES)

Abstract

The Assurance for Learning-Enabled Systems project develops an integrated well-founded approach to the co-design of cyberphysical systems with learning-enabled components and their assurance arguments for both perception and control. The approach was based on a theory of mind-predictive processing (PP) complemented with the dual process theories of reasoning. The research developed a two-layered runtime monitor using generative modeling in the first layer and graph Markov neural networks based neurosymbolic contextual modeling in the second layer.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2022
Accession Number
AD1170176

Entities

People

  • Susmit Jha

Organizations

  • SRI International

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Cyber
  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Autonomous Systems
  • Autonomous Vehicles
  • Closed Loop Systems
  • Commercial Aircraft
  • Computational Science
  • Computer Programming
  • Computer Vision
  • Computers
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Mathematical Filters
  • Motion Planning
  • Neural Networks
  • Pattern Recognition
  • Probabilistic Models
  • Unmanned Vehicles

Readers

  • Computational Linguistics
  • Distributed Systems and Data Platform Development
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks