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4 Brief Survey of Cognitive Architectures
4.2.1 SOAR
The cognitive architectures best known among AI academics are probably Soar and ACT-R,
both of which are explicitly being developed with the dual goals of creating human-level AGI
and modeling all aspects of human psychology. Neither the Soar nor ACT-R communities feel
themselves particularly near these long-term goals, yet they do take them seriously.
Soar is based on IF-THEN rules, otherwise known as “production rules.” On the surface this
makes it similar to old-style expert systems, but Soar is much more than an expert system; it’s
at minimum a sophisticated problem-solving engine. Soar explicitly conceives problem solving
as a search through solution space for a “goal state” representing a (precise or approximate)
problem solution. It uses a methodology of incremental search, where each step is supposed to
move the system a little closer to its problem-solving goal, and each step involves a potentially
complex “decision cycle.”
In the simplest case, the decision cycle has two
phases:
e Gathering appropriate information from the system’s long-term memory (LTM) into its
working memory (WM)
e A decision procedure that uses the gathered information to decide an action
If the knowledge available in LTM isn’t enough to solve the problem, then the decision
procedure invokes search heuristics like hill-climbing, which try to create new knowledge (new
production rules) that will help move the system closer to a solution. If a solution is found by
chaining together multiple production rules, then a chunking mechanism is used to combine
these rules together into a single rule for future use. One could view the chunking mechanism
as a way of converting explicit knowledge into implicit knowledge, similar to “map formation”
in CogPrime (see Chapter 42 of Part 2), but in the current Soar design and implementation it
is a fairly crude mechanism.
Tn recent years Soar has acquired a number of additional methods and modalities, including
some visual reasoning methods and some mechanisms for handling episodic and procedural
knowledge. These expand the scope of the system but the basic production rule and chunking
mechanisms as briefly described above remain the core “cognitive algorithm” of the system.
From a CogPrime perspective, what Soar offers is certainly valuable, e.g.
e heuristics for transferring knowledge from LTM into WM
e chaining and chunking of implications
e methods for interfacing between other forms of knowledge and implications
However, a very short and very partial list of the major differences between Soar and Cog-
Prime would include
e CogPrime contains a variety of other core cognitive mechanisms beyond the management
and chunking of implications
e the variety of “chunking” type methods in CogPrime goes far beyond the sort of localized
chunking done in Soar
e CogPrime is committed to representing uncertainty at the base level whereas Soar’s pro-
duction rules are crisp
e The mechanisms for LTM-WM interaction are rather different in CogPrime, being based
on complex nonlinear dynamics as represented in Economic Attention Allocation (ECAN)
e Currently Soar does not contain creativity-focused heuristics like blending or evolutionary
learning in its core cognitive dynamic.