The paper describes a novel approach to cognitive
architecture exploration in which multiple cognitive
architectures are integrated in their entirety. The goal is to
increase significantly the application breadth and utility of
cognitive architectures generally. The resulting architecture
favors a breadth-first rather than depth-first approach to
cognitive modeling by focusing on matching the broad power
of human cognition rather than any specific data set. It uses
human cognition as a functional blueprint for meeting the
requirements for general intelligence. For example, a chief
design principle is inspired by the power of human perception
and memory to reduce the effective complexity of problem
solving. Such complexity reduction is reflected in an
emphasis on integrating subsymbolic and statistical
mechanisms with symbolic ones. The architecture realizes a
“cognitive pyramid” in which the scale and complexity of a
problem is successively reduced via three computational
layers: Proto-cognition (information filtering and clustering),
Micro-cognition (memory retrieval modulated by expertise)
and Macro-cognition (knowledge-based reasoning). The
consequence of this design is that knowledge-based reasoning
is used primarily for non-routine, novel situations; more
familiar situations are handled by experience-based memory
retrieval. Filtering and clustering improve overall scalability
by reducing the elements to be considered by higher levels.
The paper describes the design of the architecture, two
prototype explorations, and evaluation and limitations.
3 Mar 2021
Source: Extended Mathematical Report
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