Abstract
A cognitive architecture specifies a computational infrastructure that defines the various regions and functions working as a whole to produce human-like intelligence [Newell, 1990]. It also defines the main connectivity and information flow between the various regions and functions. These functions and the connectivity between them in turn facilitate and provide implementation specifications for a variety of algorithms. Drawing inspirations from computational science, neuroscience, psychology and biology, a top-level cognitive architecture which models the information processing in human brain is developed. Four key design principles [Ng, 2009] inspired by the brain, namely the hierarchical structure, distributed memory, parallelism in information flow and pathways, are incorporated into the architecture. A prototype cognitive architecture is developed and it is able to bring to bear different types of knowledge to solve a problem. It has been applied to object recognition in images. The cognitive architecture is able to exploit bottom-up perceptual information, top-down contextual knowledge and visual feedback in a way similar to how human utilizes different knowledge to recognize objects in images.
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