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6.5 Goal-Oriented Dynamics in CogPrime
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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 ∧ 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 architectures 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

• 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.)
• 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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