In this article, we show how 4D/RCS incorporates and integrates multiple types of disparate knowledge representation techniques into a common, unifying architecture. The 4D/RCS architecture 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. We also look at symbolic versus 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.

ACT grew out of and semantic nets. It differs from other cognitive research on human memory. Over the years, architectures in that it also includes signals, ACT has evolved into ACT* and more recently, images, and maps in its knowledge database, ACT-R. ACT-R is being used in several research and maintains a tight real-time coupling projects in an Advanced Decision Architectures between iconic and symbolic data structures in Collaborative Technology Alliance for the U.S. its world model. The 4D/RCS architecture is also Army (Gonzalez 2003). ACT-R is also being different in its (1) focus on task decomposition used by thousands of schools across the country as the fundamental organizing principle; as an algebra tutor--an instructional system (2) level of specificity in the assignment of duties that supports learning by doing. Another wellknown and responsibilities to agents and units in and widely used architecture is Soar the behavior-generating hierarchy; and (3) emphasis (Laird, Newell, and Rosenbloom 1987). Soar on controlling real machines in realworld grew out of research on human problem solving environments.