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74 4 Brief Survey of Cognitive Architectures

“I grant the need for non-symbolic processes in some intelligent systems, but I think they sup-
plement rather than replace symbol systems. I know of no examples of reasoning, understanding
language, or generating complex plans that are best understood as being performed by systems
using exclusively non-symbolic processes....
AI systems that achieve human-level intelligence will involve a combination of symbolic and
non-symbolic processing.”

A few of the more important hybrid cognitive architectures are:

• CLARION [SZ04] is a hybrid architecture that combines a symbolic component for reason-
ing on “explicit knowledge” with a connectionist component for managing “implicit knowl-
edge.” Learning of implicit knowledge may be done via neural net, reinforcement learning,
or other methods. The integration of symbolic and subsymbolic methods is powerful, but a
great deal is still missing such as episodic knowledge and learning and creativity. Learning
in the symbolic and subsymbolic portions is carried out separately rather than dynamically
coupled, minimizing “cognitive synergy” effects.
• DUAL [NK04] is the most impressive system to come out of Marvin Minsky’s “Society of
Mind” paradigm. It features a population of agents, each of which combines symbolic and
connectionist representation, self-organizing to collectively carry out tasks such as percep-
tion, analogy and associative memory. The approach seems innovative and promising, but
it is unclear how the approach will scale to high-dimensional data or complex reasoning
problems due to the lack of a more structured high-level cognitive architecture.
• LIDA [BF09] is a comprehensive cognitive architecture heavily based on Bernard Baars’
“Global Workspace Theory”. It articulates a “cognitive cycle” integrating various forms of
memory and intelligent processing in a single processing loop. The architecture ties in well
with both neuroscience and cognitive psychology, but it deals most thoroughly with “lower
level” aspects of intelligence, handling more advanced aspects like language and reasoning
only somewhat sketchily. There is a clear mapping between LIDA structures and processes
and corresponding structures and processing in OCP; so that it’s only a mild stretch to view
CogPrime as an instantiation of the general LIDA approach that extends further both in
the lower level (to enable robot action and sensation via DeSTIN) and the higher level (to
enable advanced language and reasoning via OCP mechanisms that have no direct LIDA
analogues).
• MicroPsi [Bac09] is an integrative architecture based on Dietrich Dorner’s Psi model of mo-
tivation, emotion and intelligence. It has been tested on some practical control applications,
and also on simulating artificial agents in a simple virtual world. MicroPsi’s comprehen-
siveness and basis in neuroscience and psychology are impressive, but in the current version
of MicroPsi, learning and reasoning are carried out by algorithms that seem unlikely to
scale. OCP incorporates the Psi model for motivation and emotion, so that MicroPsi and
CogPrime may be considered very closely related systems. But similar to LIDA, MicroPsi
currently focuses on the “lower level” aspects of intelligence, not yet directly handling ad-
vanced processes like language and abstract reasoning.
• PolyScheme [Cas07] integrates multiple methods of representation, reasoning and infer-
ence schemes for general problem solving. Each Polyscheme “specialist” models a different
aspect of the world using specific representation and inference techniques, interacting with
other specialists and learning from them. Polyscheme has been used to model infant rea-
soning including object identity, events, causality, and spatial relations. The integration of

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