Evaluating Knowledge and Representation for Intelligent Control

Abstract

Knowledge and the way it is represented have a tremendous impact on the capabilities and performance of intelligent systems. There is evidence from studies of human cognitive functions that experts use multiple representations in problem solving tasks and know when to switch between representations. In this paper, we discuss the issues pertaining to what types of knowledge are required for an intelligent system, how to evaluate the knowledge and representations, and provide examples of how representation affects and even enables functionality of a system. We describe an example of an intelligent system architecture that is built upon multiple knowledge types and representations and has been applied to a variety of real-time intelligent systems.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2001
Accession Number
ADA525818

Entities

People

  • Elena R. Messina
  • James S. Albus
  • John M. Evans

Organizations

  • National Institute of Standards and Technology

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Autonomous Vehicles
  • Cognitive Science
  • Computer Programs
  • Computer Science
  • Computers
  • Control Systems
  • Engineering
  • Intelligent Systems
  • Manufacturing
  • Models
  • Probabilistic Models
  • Robots
  • Scientific Theories
  • Standards
  • Test And Evaluation

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Systems Analysis and Design