SOAR: an architecture for general intelligence
Artificial Intelligence
Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Controlling physical agents through reactive logic programming
Proceedings of the third annual conference on Autonomous Agents
The Architecture of Cognition
Brains, Behavior and Robotics
Approximate Reasoning in MAS: Rough Set Approach
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Approximate Reasoning in MAS: Rough Set Approach
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Knowledge Processing Middleware
SIMPAR '08 Proceedings of the 1st International Conference on Simulation, Modeling, and Programming for Autonomous Robots
Toward Rough-Granular Computing
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
A wistech paradigm for intelligent systems
Transactions on rough sets VI
Toward perception based computing: a rough-granular perspective
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
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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. In the context of applying the architecture to the control of autonomous vehicles, we describe the procedural and declarative types of knowledge that have been developed and applied and the value that each brings to achieving the ultimate goal of autonomous navigation. 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.