44 3 A Patternist Philosophy of Mind
analysis 4. The notion of “focused cognitive process” will be exemplified more thoroughly below, but in essence what is meant is a cognitive process that begins with a small number of items (drawn from memory) as its focus, and has as its goal discovering something about these items, or discovering something about something else in the context of these items or in a way strongly biased by these items. This is different from a global cognitive process whose goal is more broadly-based and explicitly involves all or a large percentage of the knowledge in an intelligent system’s memory store.
Among the focused cognitive processes are those governed by the so-called cognitive schematic implication
Context \ Procedure — Goal
where the Context involves sensory, episodic and/or declarative knowledge; and attentional knowledge is used to regulate how much resource is given to each such schematic implication in memory. Synergy among the learning processes dealing with the context, the procedure and the goal is critical to the adequate execution of the cognitive schematic using feasible computational resources. This sort of explicitly goal-driven cognition plays a significant though not necessarily dominant role in CogPrime, and is also related to production rules systems and other traditional AI systems, as will be articulated in Chapter 4.
The synthesis and analysis processes as we conceive them, in the general framework of SGS theory, are as follows. First, synthesis, as shown in Figure 3.1, is defined as
synthesis: Iteratively build compounds from the initial component pool using the combinators, greedily seeking compounds that seem likely to achieve the goal. Or in more detail:
1. Begin with some initial components (the initial “current pool’), an additional set of com- ponents identified as “combinators” (combination operators), and a goal function
2. Combine the components in the current pool, utilizing the combinators, to form product components in various ways, carrying out reductions as appropriate, and calculating relevant quantities associated with components as needed
3. Select the product components that seem most promising according to the goal function, and add these to the current pool (or else simply define these as the current pool)
4, Return to Step 2
And analysis, as shown in Figure 3.2, is defined as
analysis: Iteratively search (the system’s long-term memory) for component-sets that com- bine using the combinators to form the initial component pool (or subsets thereof), greedily seeking component-sets that seem likely to achieve the goal
or in more detail:
1. Begin with some components (the initial “current pool”) and a goal function
2. Seek components so that, if one combines them to form product components using the combinators and then performs appropriate reductions, one obtains (as many as possible of) the components in the current pool
4 In [GPPGO6], what is here called “analysis” was called “backward synthesis”, a name which has some advantages since it indicated that what’s happening is a form of creation; but here we have opted for the more traditional analysis/synthesis terminology
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