1. Cognitive
Architectures
Amr Kamel, Helwan University
PHD Preparatory 2015
Introduction
Cognitive architecture is an engineering
approach for modeling cognitive systems. It
introduces computational processes that
collaborate together to provide an overall
system behavior acts like certain cognitive
systems; which is person in most cases.
Cognitive architectures do not model only
the behavior; however; it models also the
structural aspects of the modeled cognitive
system.
In their book (Crowder, Carbone, & Friess,
2014) they specified what they mean by
cognitive system. They call it Synthetic,
Evolving Life Form (SELF) and it requires the
following capabilities:-
1. Act on its own;
2. Perform autonomous reasoning,
control, and analysis;
3. Find and fix problems within itself
(self-assessment, self-regulation
and self-healing)
4. Predict future situations and
determine its own internal
recommended actions, and create
or modify its own internal
automated complex memories and
processes.
What they meant by acting on its own is
decision making and planning and problem
solving. And autonomous reasoning,
analysis and control mean ability of
perception and leads to language ability
although they didn’t mention it explicitly.
However on the other hand; in his work
about defining the “unified theories of
cognition” (Newell, 1994); Newell specified
the areas that should be covered by unified
theories of cognition to include the below
list. Newell work had been used in creating
“Soar” which is one of the eldest cognitive
architectures.
• Problem solving, decision making,
routing actions
• Memory, learning, skill
• Perception, motor behavior
• Language
• Motivation, emotion
• Imagining, dreaming, daydreaming
Newell’s definition of the areas that should
be covered by unified theories of cognition
and hence cognitive architectures are
widely accepted for assessment of the
coverage of cognitive architectures.
Motivation
Although cognitive architectures topic is old
topic started since Allen Newell introduced
his vision about unified theories of
CogniƟon at 1987, the interest of
researchers in this subject is still hot and
increased last few years because of two
events.
The first event occurred at early 2011 when
IBM super computer ;which is based on
secret technology they called it “cognitive
computing”; competed with highly
intelligent persons on TV public game show
called “Jeopardy” where questions and
conversations happen between competitors

2. and Watson surprisingly won the
competition (Jeopardy! IBM Watson Day 3,
2011). The surprising part was not only
winning the game. However Watson’s
language responses were so real and similar
to human such that one cannot distinguish
its responses from human responses. IBM
defines cognitive systems as systems
applying human-like characteristics’ to
conveying and manipulating ideas. IBM says
that combining cognitive systems with the
inherent strengths of digital computing,
that they can help solve problems with
higher accuracy, more resilience, and a
massive scale over large bodies of
information (High, 2012). It could be noted
easily that Watson’s importance is its ability
of Big Data processing.
The other event happened at May 2014
when Google announced plans to unveil
100 autonomous car prototypes built from
scratch inside Google’s secret X lab. In fact
Google autonomous car isn’t the only
model however other companies unveiled
their prototypes like BMW and Mercedes.
Automobile manufactures ad technology
companies made numerous predictions for
the development of autonomous car
technology in the near future (Wikipedia,
2014).
Architectures Classification
Because of the development of numerous
cognitive architectures, the comparison
efforts are important to be competent of
distinguishing the differences between the
architectures.
A commonly used method for classifying
cognitive architectures relies on the general
approach of information processing in
architectures. In this technique
architectures are classified to three classes:
symbolic, sub-symbolic and hybrid classes
like the classification work of (DUCH,
OENTARYO, & PASQUIER, 2008). The
symbolic architectures uses symbolic based
processes (i.e. rule based production
systems) as building blocks for
architectures. Allen Newell argued that
although mankind could be other kinds
Figure 1 : Classical Soar Architecture

3. rather than symbolic system; they are at
least a symbolic system (Newell, 1994). In
contrast to symbolic approach which
ignores the connectionism, the sub-
symbolic realm relies on connectionist
modeling techniques (i.e. different types of
artificial neural networks). Finally, in the
hybrid loom both symbolic and sub-
symbolic processes are cooperating
together to get the advantages and benefits
of using both of them.
Extended classification efforts use the need
of the cognitive modeling for classification.
Biologically inspired architectures group in
addition to emotional and motivational
architectures group are also added (Joscha,
2009).
In the coming sections architecture of every
group is illustrated briefly to distinguish the
differences between them.
Soar
The structure of the classical Soar
architecture is depicted in Figure 1 above.
As shown in the figure; the architecture has
two memory levels the short term memory
and the long term memory. The long term
memory is a procedural memory includes
production rules. However; the short term
memory is a symbolic graph structure so
that objects can be represented in
properties and relations. The short term
memory is able of situation assessment via
perception and retrieval of rues long term
memory. The Soar action commands are
buffered in the short term memory. The
decision procedure process selects the
operators and detects impasses which will
be described later (Laird, 2008).
Figure 2: Soar processing Cycle

4. Long Term Memory (LTM) rules in Soar
works as an associative memory that
retrieves information relevant to the
current situation, so there is no need to
select between them and thus rules fire in
parallel (Laird, 2008). Operators are part of
current situation context which is used in
the selection of rules which propose,
evaluate and apply operators. In Soar there
are rules that propose operators that create
a data structure in working memory
representing the operator and an
acceptable preference so that the operator
can be considered for selection. There are
also rules that evaluate operators and
create other types of preferences that
prefer one operator to another or provide
some indication of the utility of the
operator for the current situation. Finally,
there are rules that apply the operator by
making changes to the working memory
that reflect the actions of the operator.
These changes may be purely internal or
execute external action to the environment
(Laird, 2008).
Impasses and sub-states or sub-goals are
generated from the soar processing cycle
illustrated in Figure 2 above. In the operator
selection process preferences are combined
and used for operators’ selection. If the
preferences are insufficient for making a
decision, an impasse arises and soar
automatically creates a sub-state in which
the goal is to resolve the impasse. In the
sub-state soar recursively uses the same
processing cycle to select and apply
operators leading to automatic, reactive
meta-reasoning. The impasses and resulting
sub-states provides a mechanism for Soar
to deliberately perform any of the functions
(elaboration, proposal, evaluation,
application) that are performed reactively
with rules (Laird, 2008).
Figure 3: Extended SOAR (SOAR 9) Architecture

5. Chunking process shown in Figure 1 is a
Soar learning mechanism that converts the
results of problem solving in sub-goals into
rules. Although chunking is a simple
mechanism it is extremely general and can
learn all the types of knowledge encoded in
rules (Laird, 2008).
Figure 3 shows the structure of Soar,
version 9. All of the new components have
been built, integrated and run with the
traditional Soar components; however, as
of yet there is not a single unified system
that has all the components running at
once. The major additions include: working
memory activation, which provides meta-
information about the recency and
usefulness of working memory elements;
reinforcement learning; which tunes the
numeric preferences of operator selection
rules; the appraisal detector, which
generates emotions, feelings, and an
internal reward signal for reinforcement
learning; semantic memory, which contains
symbolic structures representing facts;
episodic memory; which contains
temporally ordered “snapshots” of working
memory; a set of processes and memories
to support visual imagery, which includes
depictive representations in which spatial
information is inherent to the
representation; and clustering, which
dynamically creates new concepts and
symbols (Laird, 2008).
Soar’s processing cycle is still driven by
procedural knowledge encoded as
production rules. The new components
influence decision making indirectly by
retrieving or creating structures in symbolic
working memory that cause rules to match
and fire (Laird, 2008).
ACT-R
Adaptive Control of Though – Rational or
ACT-R is inspired by Allen Newell work of
“Unified Cognitive Theories” as well as Soar
architecture. However; ACR-R is also
inspired by the progress of Cognitive
Neuroscience.
The ACT theory has origins in the Human
Associative Memory (HAM) theory of
human memory (Anderson & Bower, 1973),
which attempted to develop a theory of
how memories were represented and how
those representations mediated behavior
that was observed in memory experiments.
It became apparent that this theory only
dealt with some aspects of knowledge;
(Anderson J. , Language, memory, and
thought, 1976) proposed a distinction
between declarative knowledge, which
HAM dealt with, and procedural knowledge,
which HAM did not deal with. A production
system was proposed that procedural
knowledge was implemented by production
rules (Anderson J. , ACT, A simple theory of
comples cogniƟon, 1996).
ACT-R accesses its modules through buffers
except for the procedural memory module
Figure 4: ACT-R Architecture (Budiu)

6. as shown in Figure 4. For each module a
dedicated buffer serves as the interface for
this module. The content of the buffers at a
given moment in time represents the state
of ACT-R at that moment (Budiu).
The pattern matcher searches for a
production that matches the current state
of the buffers. Only one such production
can be executed at a given moment. That
production when executed can modify the
buffers and thus change the state of the
system. Thus in ACT-R cognition unfolds as a
succession of production firings (Budiu).
ACT-R is a hybrid cognitive architecture. Its
symbolic structure is a production system;
the sub-symbolic structure is represented
by massively parallel processes that can be
summarized by a number of mathematical
equations. The sub-symbolic equations
control many of the symbolic processes. For
instance, if several productions match the
state of the buffers, a sub-symbolic utility
equation estimates the relative cost and
benefit associated with each production
and decides to select for execution the
production with the highest utility.
Similarly, whether a fact can be retrieved
from declarative memory depends on sub-
symbolic retrieval equations, which take
into account the context and the history of
usage of that fact. Sub-symbolic processes
are also responsible of for most learning
processes in ACT-R (Budiu).
Cognitive Computing
Cognitive computing aims to develop a
coherent, unified, universal mechanism
inspired by the mind’s capabilities. Rather
than assemble a collection of piecemeal
solutions, whereby different cognitive
processes are each constructed via
independent solutions, it seeks to
implement a unified computational theory
of the mind. This was the definition of
cognitive computing by (Dharmendra,
Rajagopal, Steven, Anthony, Anthony, &
Raghavendra, 2011). It seems that their
work is inspired by Allen Newell for
implementing a unified theory of cognition.
Cortical Simulator Design &
Implementation
Since 2007, IBM has developing the C2
near-real-time mammalian-scale cortical
simulator to harness the distributed
memory multiprocessor architecture of IBM
Gene systems.
The cortical simulator includes a clock-
driven component with discrete time steps,
as well as an event-driven component. In
the former, the state of the neurons is
updated once every time step, typically
either one millisecond or one-tenth of one
millisecond of simulated time. In the latter,
when a neuron fires, it creates a spike event
that is then delivered to the synapse of a
target neuron after a tunable axonal delay
(Dharmendra, Rajagopal, Steven, Anthony,
Anthony, & Raghavendra, 2011).
The entire state of the simulation
(consisting of neurons, synapses and
transient spike messages) is evenly
distributed among the local memories of
the multiprocessor system. Each processor
maintains the state of a group of neurons
and all synapses providing inputs to these
neurons. A notable C2 innovaƟon is the
memory-efficient representation of synaptic
state, facilitating significantly increased
model scales (Dharmendra, Rajagopal,

7. Steven, Anthony, Anthony, & Raghavendra,
2011).
C2 harness a large number of processors
while fully exploiting the computational
capacity of each processor to achieve near-
real-time simulation. (Dharmendra,
Rajagopal, Steven, Anthony, Anthony, &
Raghavendra, 2011).
Since 2007, simulaƟons have grown steadily
in scale, beginning with early work of at a
scale of mouse and rat cortices. However; in
May 2009 Dawn Blue Gene/P system made
an important achievement milestone of cat-
scale cortical simulations. It is roughly
equivalent to 4.5% of human scale
(Dharmendra, Rajagopal, Steven, Anthony,
Anthony, & Raghavendra, 2011).
TrueNorth Cognitive Computing
Architecture
TrueNorth is a neuromorphic CMOS chip
produced by IBM. It consists of 4096
hardware cores, each one simulaƟng 256
programmable neurons for a total of over
one million neurons. Each neuron has 256
programmable synapses which convey the
signals between them. Hence the total
number of programmable synapses is just
over 286 million (2^28). In terms of basic
building blocks, the chip hosts 5.4 billion
transistors (see Figure 5). Since memory,
computation, and communication are
handled in each one of the 4096 synapƟc
cores, TrueNorth circumvents the Von-
Neumann-architecture bottlenecks and is
very energy-efficient, boasting a power
consumpƟon of only 1/10,000 of
conventional chips (TrueNorth).
Figure 5: TrueNorth Chip Architecture

8. As described in (Andrew, et al., 2013) this
architecture conveys the following
achievements: the demonstraƟon of 256
neurons, 64K/256K-synapse neurosynaptic
cores in 45 nm silicon; second a
demonstration of multiple real-time
applications; third, Compass, a simulator of
the TrueNorth architecture, which
simulated over 2 billion neurosynapƟc cores
exceeding 10 14
synapses; and, fourth, a
visualization of the long distance
connectivity of the Macaque brain mapped
to TrueNorth architecture.
The applications created using TrueNorth
architecture are described in (Steve, et al.,
2013). The applications include speaker
recognition, music composer recognition,
digit recognition, Collision avoidance,
optical flow and eye detection. Developing
these applications on such massive
architecture required developing a new
programming paradigm for consists of (a)
an abstraction for a TrueNorth program
named Corelet, for representing a network
of neurosynaptic cores that encapsulates all
details except external inputs and outputs;
(b) an object-oriented Corelet Language for
creating, composing and decomposing
corelets; (c) a Corelet Library that acts as an
ever-growing repository of reusable
corelets from which programmers compose
new corelets; and (d) an end-to-end Corelet
Laboratory that is programming
environment which integrates with the
TrueNorth architecture simulator; Compass,
to support all aspects of programming cycle
from design, through development,
debugging and up to deployment.
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