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6.5 Goal-Oriented Dynamics in CogPrime 113
example, perhaps MOSES would discover that older males wearing ties tend not to become
regular visitors. If the new playmate is an older male wearing a tie, this is directly applicable.
But if the current playmate is wearing a tuxedo, then PLN may be helpful via reasoning that
even though a tuxedo is not a tie, it’s a similar form of fancy dress — so PLN may extend the
MOSES-learned rule to the present case and infer that the new playmate is not likely to be a
regular visitor.
6.5 Goal-Oriented Dynamics in CogPrime
CogPrime’s dynamics has both goal-oriented and “spontaneous” aspects; here for simplicity’s
sake we will focus on the goal-oriented ones. The basic goal-oriented dynamic of the CogPrime
system, within which the various types of memory are utilized, is driven by implications known
as “cognitive schematics”, which take the form
Context \ Procedure > Goal < p>
(summarized C A P > G). Semi-formally, this implication may be interpreted to mean: “If the
context C' appears to hold currently, then if I enact the procedure P, I can expect to achieve the
goal G with certainty p.” Cognitive synergy means that the learning processes corresponding to
the different types of memory actively cooperate in figuring out what procedures will achieve
the system’s goals in the relevant contexts within its environment.
CogPrime’s cognitive schematic is significantly similar to production rules in classical ar-
chitectures like SOAR and ACT-R (as reviewed in Chapter 4; however, there are significant
differences which are important to CogPrime’s functionality. Unlike with classical production
rules systems, uncertainty is core to CogPrime’s knowledge representation, and each CogPrime
cognitive schematic is labeled with an uncertain truth value, which is critical to its utilization by
CogPrime’s cognitive processes. Also, in CogPrime, cognitive schematics may be incomplete,
missing one or two of the terms, which may then be filled in by various cognitive processes
(generally in an uncertain way). A stronger similarity is to MicroPsi’s triplets; the differences
in this case are more low-level and technical and have already been mentioned in Chapter 4.
Finally, the biggest difference between CogPrime’s cognitive schematics and production rules
or other similar constructs, is that in CogPrime this level of knowledge representation is not
the only important one. CLARION [SZ04], as reviewed above, is an example of a cognitive
architecture that uses production rules for explicit knowledge representation and then uses a
totally separate subsymbolic knowledge store for implicit knowledge. In CogPrime
both explicit and implicit knowledge are stored in the same graph of nodes and links, with
e explicit knowledge stored in probabilistic logic based nodes and links such as cognitive
schematics (see Figure 6.8 for a depiction of some explicit linguistic knowledge.)
e implicit knowledge stored in patterns of activity among these same nodes and links, defined
via the activity of the “importance” values (see Figure 6.9 for an illustrative example thereof)
associated with nodes and links and propagated by the ECAN attention allocation process
The meaning of a cognitive schematic in CogPrime is hence not entirely encapsulated in its
explicit logical form, but resides largely in the activity patterns that ECAN causes its activation
or exploration to give rise to. And this fact is important because the synergetic interactions
of system components are in large part modulated by ECAN activity. Without the real-time
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