Supervisory Control System for Ship Damage Control: Volume 1 - Design Overview

Abstract

This report presents a state-of-the-art concept of automated situation awareness for ship damage control. The solution encompasses model-based crisis recognition, model-based predictive validation, automated casualty response, and a supervisor interface console. The report details a solution approach to the Supervisory Control decision-making task for damage control management of fire, smoke, flooding, pipe rupture, and stability aboard ships. The solution is relevant to ships built in the future, as it assumes intelligent sensors and actuators that are more advanced than those that exist on any current ship. The solution encompasses all three stages of Supervisory Control: model-based crisis recognition, model-based predictive validation, and automated casualty response. This report also describes an approach to the creation and evaluation of a Supervisor-Operator Override Console because of its integral relationship to Situation Assessment. The described Supervisory Control solution is designed to function iii vitro, by use of the existing Illinois Damage Control Scenario Generator armed with simulated smart sensors and actuators. And it is designed to function in vivo, in live crisis exercises aboard a floating research laboratory called the ex-USS Shadwell. The three major research areas of the described Supervisory Control solution are knowledge-based expert systems, machine learning in noisy domains, and assessment of supervisory human-computer interfaces.

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Document Details

Document Type
Technical Report
Publication Date
Jul 27, 2001
Accession Number
ADA392798

Entities

People

  • David C. Wilkins
  • Frederic W. Williams
  • Janet A. Sniezek
  • Patricia A. Tetem

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Ground and Sea Platforms
  • Human Systems
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Automata Theory
  • Bayesian Networks
  • Cognition
  • Cognitive Systems Engineering
  • Cognitive Workload
  • Computational Science
  • Computer Programming
  • Computers
  • Control Systems
  • Human Factors Engineering
  • Human Systems Integration
  • Information Systems
  • Machine Learning
  • Psychology
  • Reasoning
  • Web Browsers

Readers

  • Database Systems and Applications
  • Fire Suppression Systems Design.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems