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Chapter 13
Local, Global and Glocal Knowledge
Representation
Co-authored with Matthew Ikle, Joel Pitt and Rui Liu
13.1 Introduction
One of the most powerful metaphors we’ve found for understanding minds is to view them
as networks — i.e. collections of interrelated, interconnected elements. The view of mind as
network is implicit in the patternist philosophy, because every pattern can be viewed as a
pattern in something, or a pattern of arrangement of something — thus a pattern is always
viewable as a relation between two or more things. A collection of patterns is thus a pattern-
network. Knowledge of all kinds may be given network representations; and cognitive processes
may be represented as networks also; for instance via representing them as programs, which
may be represented as trees or graphs in various standard ways. The emergent patterns arising
in an intelligence as it develops may be viewed as a pattern network in themselves; and the
relations between an embodied mind and its physical and social environment may be viewed in
terms of ecological and social networks.
The chapters in this section are concerned with various aspects of networks, as related to
intelligence in general and AGI in particular. Most of this material is not specific to CogPrime,
and would be relevant to nearly any system aiming at human-level AGI. However, most of it
has been developed in the course of work on CogPrime, and has direct relevance to under-
standing the intended operation of various aspects of a completed CogPrime system. We begin
our excursion into networks, in this chapter, with an issue regarding networks and knowledge
representation. One of the biggest decisions to make in designing an AGI system is how the
system should represent knowledge. Naturally any advanced AGI system is going to synthesize
a lot of its own knowledge representations for handling particular sorts of knowledge — but
still, an AGI design typically makes at least some sort of commitment about the category of
knowledge representation mechanisms toward which the AGI system will be biased. The two
major supercategories of knowledge representation systems are local (also called explicit) and
global (also called implicit) systems, with a hybrid category we refer to as glocal that combines
both of these. In a local system, each piece of knowledge is stored using a small percentage of
AGI system elements; in a global system, each piece of knowledge is stored using a particular
pattern of arrangement, activation, etc. of a large percentage of AGI system elements; in a
glocal system, the two approaches are used together.
In the first section here we discuss the symbolic, semantic-network aspects of knowledge
representation in CogPrime
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