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Cognitive Architecture
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A cognitive architecture can refer to a theory about the structure of the human
mind. One of the main goals of a cognitive architecture is to summarize the
various results of cognitive psychology in a comprehensive computer model.
However, the results need to be in a formalized form so far that they can be the
basis of a computer program. By combining the individual results are so for a
comprehensive theory of cognition and the other a commercially usable model
arise. Successful cognitive architectures include ACT-R (Adaptive Control of
Thought, ACT), SOAR and OpenCog.
History
Herbert A. Simon, one of the founders of the field of artificial intelligence,
stated that the 1960 thesis by his student Ed Feigenbaum, EPAM provided a
possible "architecture for cognition"[1] because it included some commitments
for how more than one fundamental aspect of the human mind worked. In
EPAM's case, human memory and human learning.
John R. Anderson started research on human memory in the early 1970s and his
1973 thesis with Gordon H. Bower provided a theory of human associative
memory. He included more aspects of his research on long-term memory and
thinking processes into this research and eventually designed a cognitive
architecture he eventually called ACT. He and his student used the term
"cognitive architecture" in his lab to refer to the ACT theory as embodied in the
collection of papers and designs since they didn't yet have any sort of complete
implementation at the time.
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In 1983 John R. Anderson published the seminal work in this area, entitled The
Architecture of Cognition. One can distinguish between the theory of cognition
and the implementation of the theory. The theory of cognition outlined the
structure of the various parts of the mind and made commitments to the use of
rules, associative networks, and other aspects. The cognitive architecture
implements the theory on computers. The software used to implement the
cognitive architectures were also "cognitive architectures". Thus, a cognitive
architecture can also refer to a blueprint for intelligent agents. It proposes
(artificial) computational processes that act like certain cognitive systems, most
often, like a person, or acts intelligent under some definition. Cognitive
architectures form a subset of general agent architectures. The term
'architecture' implies an approach that attempts to model not only behavior, but
also structural properties of the modelled system.
Distinctions
Cognitive architectures can be symbolic, connectionist, or hybrid. Some
cognitive architectures or models are based on a set of generic rules, as, e.g., the
Information Processing Language (e.g., Soar based on the unified theory of
cognition, or similarly ACT-R). Many of these architectures are based on themind-is-like-a-computer analogy. In contrast subsymbolic processing specifies
no such rules a priori and relies on emergent properties of processing units (e.g.
nodes). Hybrid architectures combine both types of processing (such as
CLARION). A further distinction is whether the architecture is centralized with
a neural correlate of a processor at its core, or decentralized (distributed). The
decentralized flavor, has become popular under the name of parallel distributed
processing in mid-1980s and connectionism, a prime example being neural
networks. A further design issue is additionally a decision between holistic and
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atomistic, or (more concrete) modular structure. By analogy, this extends to
issues of knowledge representation.
In traditional AI, intelligence is often programmed from above: the programmer
is the creator, and makes something and imbues it with its intelligence, though
many traditional AI systems were also designed to learn (e.g. improving their
game-playing
or
problem-solving
competence).
Biologically
inspired
computing, on the other hand, takes sometimes a more bottom-up, decentralised
approach; bio-inspired techniques often involve the method of specifying a set
of simple generic rules or a set of simple nodes, from the interaction of which
emerges the overall behavior. It is hoped to build up complexity until the end
result is something markedly complex (see complex systems). However, it is
also arguable that systems designed top-down on the basis of observations of
what humans and other animals can do rather than on observations of brain
mechanisms, are also biologically inspired, though in a different way.
Some well-known cognitive architectures
A comprehensive review of implemented cognitive architectures has been
undertaken in 2010 by Samsonovish et. al.[4] and is available as an online
repository. Some well-known cognitive architectures, in alphabetical order:
 4CAPS, developed at Carnegie Mellon University under Marcel A. Just
 ACT-R, developed at Carnegie Mellon University under John R.
Anderson.
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 ALifeE, developed under Toni Conde at the Ecole Polytechnique
Fédérale de Lausanne.
 Apex developed under Michael Freed at NASA Ames Research Center.
 ASMO, developed under Rony Novianto at University of Technology,
Sydney.
 CHREST, developed under Fernand Gobet at Brunel University and Peter
C. Lane at the University of Hertfordshire.
 CLARION the cognitive architecture, developed under Ron Sun at
Rensselaer Polytechnic Institute and University of Missouri.
 CMAC - The Cerebellar Model Articulation Controller (CMAC) is a type
of neural network based on a model of the mammalian cerebellum. It is a
type of associative memory. The CMAC was first proposed as a function
modeler for robotic controllers by James Albus in 1975 and has been
extensively used in reinforcement learning and also as for automated
classification in the machine learning community.
 CMatie is a ‘conscious’ software agent developed to manage seminar
announcements in the Mathematical Sciences Department at the
University of Memphis. It's based on Sparse distributed memory
augmented with the use of genetic algorithms as an associative memory.
 Copycat, by Douglas Hofstadter and Melanie Mitchell at the Indiana
University.
 DUAL, developed at the New Bulgarian University under Boicho
Kokinov.
 EPIC, developed under David E. Kieras and David E. Meyer at the
University of Michigan.
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 FORR developed by Susan L. Epstein at The City University of New
York.
 GAIuS developed by Sevak Avakians.
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