Multirepresentational Architectures for Human-Level Intelligence: Papers from the AAAI Fall Symposium
Unmesh Kurup and B. Chandrasekaran, Cochairs
A multiplicity of representational frameworks has been proposed for explaining and creating human-level intelligence. Each has been proven useful or effective for some class of problems, but not across the board. This fact has led researchers to propose that perhaps the underlying design of cognition is multi-representational, or hybrid, and made up of subsystems with different representations and processes interacting to produce the complexity of cognition. Recent work in cognitive architectures has explored the design and use of such systems in high-level cognition. The main aim of this symposium was to bring together researchers who work on systems utilizing different types of representations to explore a range of questions about the theoretical framework and applications of such systems.