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Using 4D/RCS to Address AI Knowledge Integration

Published

Author(s)

Craig I. Schlenoff, James S. Albus, Elena R. Messina, Tony Barbera, Rajmohan Madhavan, Stephen B. Balakirsky

Abstract

In this paper, we show how 4D/RCS incorporates and integrates multiple types of disparate knowledge representation techniques into a common, unifying architecture. 4D/RCS is based on the supposition that different knowledge representation techniques offer different advantages, and 4D/RCS is designed in such a way as to combine the strengths of all of these techniques into a common unifying architecture in order to exploit the advantages of each. In the context of applying the architecture to the control of autonomous vehicles, we describe the procedural and declarative types of knowledge that has been developed and applied, and value that each brings to the achieving the ultimate goal of autonomous navigation. We also look at symbolic vs. iconic knowledge representation, and show how 4D/RCS accommodates both of these types of representations and uses the strengths of each to strive towards achieving human-level intelligence in autonomous systems.
Citation
Submitted to the Special Issue of AI Magazine: Achieving Human-Level Intelligent through Integrated Systems and Research.

Keywords

4D/RCS, autonomous navigation, declarative, iconic, knowledge, Knowledge Engineering, procedural, Robotics & Intelligent Systems, symbolic

Citation

Schlenoff, C. , Albus, J. , Messina, E. , Barbera, T. , Madhavan, R. and Balakirsky, S. (2006), Using 4D/RCS to Address AI Knowledge Integration, Submitted to the Special Issue of AI Magazine: Achieving Human-Level Intelligent through Integrated Systems and Research., [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822668 (Accessed September 24, 2021)
Created June 26, 2006, Updated February 19, 2017