Messina, E.
, Albus, J.
, Schlenoff, C.
and Evans, J.
(2003),
Knowledge Engineering for Real Time Intelligent Control, Journal of Intelligent & Fuzzy Systems, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822497
(Accessed November 23, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Knowledge Engineering for Real Time Intelligent Control
Published
Author(s)
, , , J L. Evans
Abstract
The key to real-time intelligent control lies in the knowledge models that the system contains. We argue that there needs to be a more rigorous approach to engineering the knowledge within intelligent controllers. Three main classes of knowledge are identified: parametric, geometric/iconic, and symbolic. Each of these classes provides unique perspectives and advantages for the planning of behaviors by the intelligent system. Examples of each from demonstration systems are presented.Citation
Journal of Intelligent & Fuzzy Systems
Volume
14
Pub Type
Journals
Download Paper
Keywords
geometic, iconic, intelligent control, Knowledge Engineering, knowledge representation, parametic, Robotics & Intelligent Systems, spatial, Unmanned System
Citation
Issues
If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.
Created December 31, 2003, Updated February 17, 2017