RTOP: A Conceptual and Computational Framework for General Intelligence
  A novel general intelligence model is proposed with three types of learning.
A unified sequence of the foreground percept trace and the command trace
translates into direct and time-hop observation paths to form the basis of Raw
learning. Raw learning includes the formation of image-image associations,
which lead to the perception of temporal and spatial relationships among
objects and object parts; and the formation of image-audio associations, which
serve as the building blocks of language. Offline identification of similar
segments in the observation paths and their subsequent reduction into a common
segment through merging of memory nodes leads to Generalized learning.
Generalization includes the formation of interpolated sensory nodes for robust
and generic matching, the formation of sensory properties nodes for specific
matching and superimposition, and the formation of group nodes for simpler
logic pathways. Online superimposition of memory nodes across multiple
predictions, primarily the superimposition of images on the internal projection
canvas, gives rise to Innovative learning and thought. The learning of actions
happens the same way as raw learning while the action determination happens
through the utility model built into the raw learnings, the utility function
being the pleasure and pain of the physical senses.
