Consensus Theory in Expert Systems: An Adaptive Inference Framework and Application to Image Understanding

Abstract

Advances in automated image understanding technology are essential to our ability to exploit today's sophisticated imagery capabilities to support battlefield intelligence requirements. This report describes the application of a unique inference framework, Non-Monotonic Probabilist, to the problem of achieving consensus among modules, each of which supports a different part of the image understanding problem. Non-Monotonic Probabilist combines symbolic default

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1988
Accession Number
ADA204253

Entities

People

  • James R. Mcintyre
  • Kathryn B. Laskey
  • Marvin S. Cohen
  • Paul K. Black
  • William G. Roman

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artillery
  • Cognitive Science
  • Computer Languages
  • Computers
  • Expert Systems
  • Identification
  • Image Processing
  • Image Registration
  • Inference Engines
  • Lisp Programming Language
  • Reasoning
  • Recognition
  • Right Angles
  • Security
  • Target Recognition

Fields of Study

  • Computer science

Readers

  • Academic Conference Management
  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy