• SOAR [LRN87], a classic example of expert rule-based cognitive architecture designed to
model general intelligence. It has recently been extended to handle sensorimotor functions,
though in a somewhat cognitively unnatural way; and is not yet strong in areas such as
episodic memory, creativity, handling uncertain knowledge, and reinforcement learning.
• ACT-R [AL03] is fundamentally a symbolic system, but Duch classifies it as a hybrid system
because it incorporates connectionist-style activation spreading in a significant role; and
there is an experimental thoroughly connectionist implementation to complement the primary
mainly-symbolic implementation. Its combination of SOAR-style “production rules”
with large-scale connectionist dynamics allows it to simulate a variety of human psychological
phenomena, but abstract reasoning, creativity and transfer learning are still missing.
• EPIC [RCK01], a cognitive architecture aimed at capturing human perceptual, cognitive
and motor activities through several interconnected processors working in parallel. The
system is controlled by production rules for cognitive processors and a set of perceptual
(visual, auditory, tactile) and motor processors operating on symbolically coded features
rather than raw sensory data. It has been connected to SOAR for problem solving, planning
and learning,
• ICARUS [Lan05], an integrated cognitive architecture for physical agents, with knowledge
specified in the form of reactive skills, each denoting goal-relevant reactions to a class of
problems. The architecture includes a number of modules: a perceptual system, a planning
system, an execution system, and several memory systems. Concurrent processing is absent,
attention allocation is fairly crude, and uncertain knowledge is not thoroughly handled.
• SNePS (Semantic Network Processing System) [SE07] is a logic, frame and network-based
knowledge representation, reasoning, and acting system that has undergone over three
decades of development. While it has been used for some interesting prototype experiments
in language processing and virtual agent control, it has not yet been used for any
large-scale or real-world application.
• Cyc [LG90] is an AGI architecture based on predicate logic as a knowledge representation,
and using logical reasoning techniques to answer questions and derive new knowledge from
old. It has been connected to a natural language engine, and designs have been created
for the connection of Cyc with Albus’s 4D-RCS [AM01]. Cyc’s most unique aspect is the
large database of commonsense knowledge that Cycorp has accumulated (millions of pieces
of knowledge, entered by specially trained humans in predicate logic format); part of the
philosophy underlying Cyc is that once a sufficient quantity of knowledge is accumulated in
the knowledge base, the problem of creating human-level general intelligence will become
much less difficult due to the ability to leverage this knowledge.
While these architectures contain many valuable ideas and have yielded some interesting results,
we feel they are incapable on their own of giving rise to the emergent structures and dynamics
required to yield humanlike general intelligence using feasible computational resources. However,
we are more sanguine about the possibility of ideas and components from symbolic architectures
playing a role in human-level AGI via incorporation in hybrid architectures.
We now review a few symbolic architectures in slightly more detail.
60 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:
• Gathering appropriate information from the system’s long-term memory (LTM) into its
working memory (WM)
• 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.
In 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.
• heuristics for transferring knowledge from LTM into WM
• chaining and chunking of implications
• 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
• CogPrime contains a variety of other core cognitive mechanisms beyond the management
and chunking of implications
• the variety of “chunking” type methods in CogPrime goes far beyond the sort of localized
chunking done in Soar
• CogPrime is committed to representing uncertainty at the base level whereas Soar’s production
rules are crisp
• The mechanisms for LTM-WM interaction are rather different in CogPrime, being based
on complex nonlinear dynamics as represented in Economic Attention Allocation (ECAN)
• Currently Soar does not contain creativity-focused heuristics like blending or evolutionary
learning in its core cognitive dynamic.
4.2 Symbolic Cognitive Architectures 61
4.2.2 ACT-R
In the grand scope of cognitive architectures, ACT-R is quite similar to Soar, but there are
many micro-level differences. ACT-R is defined in terms of declarative and procedural knowledge,
where procedural knowledge takes the form of Soar-like production rules, and declarative
knowledge takes the form of chunks. It contains a variety of mechanisms for learning new rules
and chunks from old; and also contains sophisticated probabilistic equations for updating the
activation levels associated with items of knowledge (these equations being roughly analogous
in function to, though quite different from, the ECAN equations in CogPrime).
Figure 4.2 displays the current architecture of ACT-R. The flow of cognition in the system is
in response to the current goal, currently active information from declarative memory, information
attended to in perceptual modules (vision and audition are implemented), and the current
state of motor modules (hand and speech are implemented). The early work with ACT-R was
based on comparing system performance to human behavior, using only behavioral measures,
such as the timing of keystrokes or patterns of eye movements. Using such measures, it was not
possible to test detailed assumptions about which modules were active in the performance of
a task. More recently the ACT-R community has been engaged in a process of using imaging
data to provide converging data on module activity. Figure 4.3 illustrates the associations they
have made between the modules in Figure 4.2 and brain regions. Coordination among all of
these components occurs through actions of the procedural module, which is mapped to the
basal ganglia.
Fig. 4.2: High-level architecture of ACT-R
In practice ACT-R, even more so than Soar, seems to be used more as a programming
framework for cognitive modeling than as an AI system. One can fairly easily use ACT-R
to program models of specific human mental behaviors, which may then be matched against
62 4 Brief Survey of Cognitive Architectures
Fig. 4.3: Conjectured Mapping Between ACT-R and the Brain
psychological data. Opinions differ as to whether this sort of modeling is valuable for achieving
AGI goals. CogPrime is not designed to support this kind of modeling, as it intentionally does
many things very differently from humans.
ACT-R in its original form did not say much about perceptual and motor operations, but
recent versions have incorporated EPIC, an independent cognitive architecture focused on modeling
these aspects of human behavior.
4.2.3 Cyc and Texai
Our review of cognitive architectures would be incomplete without mentioning Cyc [LG90],
one of the best known and best funded AGI-oriented projects in history. While the main focus
of the Cyc project has been on the hand-coding of large amounts of declarative knowledge,
there is also a cognitive architecture of sorts there. The center of Cyc is an engine for logical
deduction, acting on knowledge represented in predicate logic. A natural language engine has
been associated with the logic engine, which enables one to ask English questions and get
English replies.
Stephen Reed, while an engineer at Cycorp, designed a perceptual-motor front end for Cyc
based on James Albus’ Reference Model Architecture; the ensuing system, called Cognitive-
Cyc, would have been the first full-fledged cognitive architecture based on Cyc, but was not
implemented. Reed left Cycorp and is now building a system called Texai, which has many
similarities to Cyc (and relies upon the OpenCyc knowledge base, a subset of Cyc’s overall
knowledge base), but incorporates a CognitiveCyc style cognitive architecture.
4.2 Symbolic Cognitive Architectures 63
4.2.4 NARS
Pei Wang’s NARS logic [Wan06] played a large role in the development of PLN, CogPrime’s
uncertain logic component, a relationship that is discussed in depth in [GMIH08] and won’t
be re-emphasized here. However, NARS is more than just an uncertain logic, it is also an
overall cognitive architecture (which is centered on NARS logic, but also includes other aspects).
CogPrime bears little relation to NARS except in the specific similarities between PLN logic
and NARS logic, but, the other aspects of NARS are worth briefly recounting here.
NARS is formulated as a system for processing tasks, where a task consists of a question or a
piece of new knowledge. The architecture is focused on declarative knowledge, but some pieces
of knowledge may be associated with executable procedures, which allows NARS to carry out
control activities (in roughly the same way that a Prolog program can).
At any given time a NARS system contains
• working memory: a small set of tasks which are active, kept for a short time, and closely
related to new questions and new knowledge
• long-term memory: a huge set of knowledge which is passive, kept for a long time, and not
necessarily related to current questions and knowledge
The working and long term memory spaces of NARS may each be thought of as a set of
chunks, where each chunk consists of a set of tasks and a set of knowledge. NARS’s basic
cognitive process is:
1. choose a chunk
2. choose a task from that chunk
3. choose a piece of knowledge from that chunk
4. use the task and knowledge to do inference
5. send the new tasks to corresponding chunks
Depending on the nature of the task and knowledge, the inference involved may be one of
the following:
• if the task is a question, and the knowledge happens to be an answer to the question, a
copy of the knowledge is generated as a new task
• backward inference
• revision (merging two pieces of knowledge with the same form but different truth value)
• forward inference
• execution of a procedure associated with a piece of knowledge
Unlike many other systems, NARS doesn’t decide what type of inference is used to process
a task when the task is accepted, but works in a data-driven way – that is, it is the task and
knowledge that dynamically determine what type of inference will be carried out
The “choice” processes mentioned above are done via assigning relative priorities to
• chunks (where they are called activity)
• tasks (where they are called urgency)
• knowledge (where they are called importance)
64 4 Brief Survey of Cognitive Architectures
and then distributing the system’s resources accordingly, based on a probabilistic algorithm.
(It’s interesting to note that while NARS uses probability theory as part of its control mechanism,
the logic it uses to represent its own knowledge about the world is nonprobabilistic. This
is considered conceptually consistent, in the context of NARS theory, because system control
is viewed as a domain where the system’s knowledge is more complete, thus more amenable to
probabilistic reasoning.)
4.2.5 GLAIR and SNePS
Another logic-focused cognitive architecture, very different from NARS in detail, is Stuart
Shapiro’s GLAIR cognitive architecture, which is centered on the SNePS paraconsistent logic
[SE07].
Like NARS, the core “cognitive loop” of GLAIR is based on reasoning: either thinking about
some percept (e.g. linguistic input, or sense data from the virtual or physical world), or answering
some question. This inference based cognition process is turned into an intelligent agent
control process via coupling it with an acting component, which operates according to a set of
policies, each one of which tells the system when to take certain internal or external actions
(including internal reasoning actions) in response to its observed internal and external situation.
GLAIR contains multiple layers:
• the Knowledge Layer (KL), which contains the beliefs of the agent, and is where reasoning,
planning, and act selection are performed
• the Sensori-Actuator Layer (SAL), contains the controllers of the sensors and effectors of
the hardware or software robot.
• the Perceptuo-Motor Layer (PML), which grounds the KL symbols in perceptual structures
and subconscious actions, contains various registers for providing the agent’s sense of situatedness
in the environment, and handles translation and communication between the KL
and the SAL.
The logical Knowledge Layer incorporates multiple memory types using a common representation
(including declarative, procedural, episodic, attentional and intentional knowledge, and
meta-knowledge). To support this broad range of knowledge types, a broad range of logical inference
mechanisms are used, so that the KL may be variously viewed as predicate logic based,
frame based, semantic network based, or from other perspectives.
What makes GLAIR more robust than most logic based AI approaches is the novel paraconsistent
logical formalism used in the knowledge base, which means (among other things)
that uncertain, speculative or erroneous knowledge may exist in the system’s memory without
leading the system to create a broadly erroneous view of the world or carry out egregiously
unintelligent actions. CogPrime is not thoroughly logic-focused like GLAIR is, but in its logical
aspect it seeks a similar robustness through its use of PLN logic, which embodies properties
related to paraconsistency.
Compared to CogPrime, we see that GLAIR has a similarly integrative approach, but that
the integration of different sorts of cognition is done more strictly within the framework of
logical knowledge representation.
4.3 Emergentist Cognitive Architectures 65
4.3 Emergentist Cognitive Architectures
Another species of cognitive architecture expects abstract symbolic processing to emerge from
lower-level “subsymbolic” dynamics, which sometimes (but not always) are designed to simulate
neural networks or other aspects of human brain function. These architectures are typically
strong at recognizing patterns in high-dimensional data, reinforcement learning and associative
memory; but no one has yet shown how to achieve high-level functions such as abstract reasoning
or complex language processing using a purely subsymbolic approach. A few of the more
important subsymbolic, emergentist cognitive architectures are:
• DeSTIN [ARK09a, ARC09], which is part of CogPrime, may also be considered as an
autonomous AGI architecture, in which case it is emergentist and contains mechanisms
to encourage language, high-level reasoning and other abstract aspects of intelligent to
emerge from hierarchical pattern recognition and related self-organizing network dynamics.
In CogPrime DeSTIN is used as part of a hybrid architecture, which greatly reduces the
reliance on DeSTIN’s emergent properties.
• Hierarchical Temporal Memory (HTM) [HB06] is a hierarchical temporal pattern
recognition architecture, presented as both an AI approach and a model of the cortex. So
far it has been used exclusively for vision processing and we will discuss its shortcomings
later in the context of our treatment of DeSTIN.
• SAL [JL08], based on the earlier and related IBCA (Integrated Biologically-based Cognitive
Architecture) is a large-scale emergent architecture that seeks to model distributed
information processing in the brain, especially the posterior and frontal cortex and the
hippocampus. So far the architectures in this lineage have been used to simulate various
human psychological and psycholinguistic behaviors, but haven’t been shown to give rise to
higher-level behaviors like reasoning or subgoaling.
• NOMAD (Neurally Organized Mobile Adaptive Device) automata and its successors
[KE06] are based on Edelman’s “Neural Darwinism” model of the brain, and feature large
numbers of simulated neurons evolving by natural selection into configurations that carry
out sensorimotor and categorization tasks. The emergence of higher-level cognition from
this approach seems rather unlikely.
• Ben Kuipers and his colleagues [MK07, MK08, MK09]have pursued an extremely innovative
research program which combines qualitative reasoning and reinforcement learning to enable
an intelligent agent to learn how to act, perceive and model the world. Kuipers’ notion of
“bootstrap learning” involves allowing the robot to learn almost everything about its world,
including for instance the structure of 3D space and other things that humans and other
animals obtain via their genetic endowments. Compared to Kuipers’ approach, CogPrime
falls in line with most other approaches which provide more “hard-wired” structure, following
the analogy to biological organisms that are born with more innate biases.
There is also a set of emergentist architectures focused specifically on developmental robotics,
which we will review below in a separate subsection, as all of these share certain common
characteristics.
Our general perspective on the emergentist approach is that it is philosophically correct
but currently pragmatically inadequate. Eventually, some emergentist approach could surely
succeed at giving rise to humanlike general intelligence – the human brain, after all, is plainly
an emergentist system. However, we currently lack understanding of how the brain gives rise
to abstract reasoning and complex language, and none of the existing emergentist systems
66 4 Brief Survey of Cognitive Architectures
seem remotely capable of giving rise to such phenomena. It seems to us that the creation of
a successful emergentist AGI will have to wait for either a detailed understanding of how the
brain gives rise to abstract thought, or a much more thorough mathematical understanding of
the dynamics of complex self-organizing systems.
The concept of cognitive synergy is more relevant to emergentist than to symbolic architectures.
In a complex emergentist architecture with multiple specialized components, much of
the emergence is expected to arise via synergy between different richly interacting components.
Symbolic systems, at least in the forms currently seen in the literature, seem less likely to give
rise to cognitive synergy as their dynamics tend to be simpler. And hybrid systems, as we shall
see, are somewhat diverse in this regard: some rely heavily on cognitive synergies and others
consist of more loosely coupled components.
We now review the DeSTIN emergentist architecture in more detail, and then turn to the
developmental robotics architectures.
4.3.1 DeSTIN: A Deep Reinforcement Learning Approach to AGI
The DeSTIN architecture, created by Itamar Arel and his colleagues, addresses the problem
of general intelligence using hierarchical spatiotemporal networks designed to enable scalable
perception, state inference and reinforcement-learning-guided action in real-world environments.
DeSTIN has been developed with the plan of gradually extending it into a complete system for
humanoid robot control, founded on the same qualitative information-processing principles as
the human brain (though without striving for detailed biological realism). However, the practical
work with DeSTIN to date has focused on visual and auditory processing; and in the context of
the present proposal, the intention is to utilize DeSTIN for perception and actuation oriented
processing, hybridizing it with CogPrime which will handle abstract cognition and language.
Here we will discuss DeSTIN primarily in the perception context, only briefly mentioning the
application to actuation which is conceptually similar.
In DeSTIN (see Figure 4.4), perception is carried out by a deep spatiotemporal inference
network, which is connected to a similarly architected critic network that provides feedback on
the inference network’s performance, and an action network that controls actuators based on the
activity in the inference network (Figure 4.5 depicts a standard action hierarchy, of which the
hierarchy in DeSTIN is an example). The nodes in these networks perform probabilistic pattern
recognition according to algorithms to be described below; and the nodes in each of the networks
may receive states of nodes in the other networks as inputs, providing rich interconnectivity
and synergetic dynamics.
4.3.1.1 Deep versus Shallow Learning for Perceptual Data Processing
The most critical feature of DeSTIN is its uniquely robust approach to modeling the world
based on perceptual data. Mimicking the efficiency and robustness by which the human brain
analyzes and represents information has been a core challenge in AI research for decades. For
instance, humans are exposed to massive amounts of visual and auditory data every second
of every day, and are somehow able to capture critical aspects of it in a way that allows for
appropriate future recollection and action selection. For decades, it has been known that the
4.3 Emergentist Cognitive Architectures 67
Fig. 4.4: High-level architecture of DeSTIN
brain is a massively parallel fabric, in which computation processes and memory storage are
highly distributed. But massive parallelism is not in itself a solution – one also needs the right
architecture; which DeSTIN provides, building on prior work in the area of deep learning.
Humanlike intelligence is heavily adapted to the physical environments in which humans
evolved; and one key aspect of sensory data coming from our physical environments is its
hierarchical structure. However, most machine learning and pattern recognition systems are
“shallow” in structure, not explicitly incorporating the hierarchical structure of the world in
their architecture. In the context of perceptual data processing, the practical result of this is
the need to couple each shallow learner with a pre-processing stage, wherein high-dimensional
sensory signals are reduced to a lower-dimension feature space that can be understood by the
shallow learner. The hierarchical structure of the world is thus crudely captured in the hierarchy
of “preprocessor plus shallow learner.” In this sort of approach, much of the intelligence of the
system shifts to the feature extraction process, which is often imperfect and always applicationdomain
specific.
Deep machine learning has emerged as a more promising framework for dealing with complex,
high-dimensional real-world data. Deep learning systems possess a hierarchical structure that
intrinsically biases them to recognize the hierarchical patterns present in real-world data. Thus,
they hierarchically form a feature space that is driven by regularities in the observations, rather
than by hand-crafted techniques. They also offer robustness to many of the distortions and
transformations that characterize real-world signals, such as noise, displacement, scaling, etc.
Deep belief networks [HOT06] and Convolutional Neural Networks [LBDE90] have been
demonstrated to successfully address pattern inference in high dimensional data (e.g. images).
They owe their success to their underlying paradigm of partitioning large data structures into
smaller, more manageable units, and discovering the dependencies that may or may not exist
68 4 Brief Survey of Cognitive Architectures
Fig. 4.5: A standard, general-purpose hierarchical control architecture. DeSTIN’s control hierarchy
exemplifies this architecture, with the difference lying mainly in the DeSTIN control
hierarchy’s tight integration with the state inference (perception) and critic (reinforcement)
hierarchies.
between such units. However, this paradigm has its limitations; for instance, these approaches
do not represent temporal information with the same ease as spatial structure. Moreover, some
key constraints are imposed on the learning schemes driving these architectures, namely the
need for layer-by-layer training, and oftentimes pre-training. DeSTIN overcomes the limitations
of prior deep learning approaches to perception processing, and also extends beyond perception
to action and reinforcement learning.
4.3.1.2 DeSTIN for Perception Processing
The hierarchical architecture of DeSTIN’s spatiotemporal inference network comprises an arrangement
into multiple layers of “nodes” comprising multiple instantiations of an identical
cortical circuit. Each node corresponds to a particular spatiotemporal region, and uses a statistical
learning algorithm to characterize the sequences of patterns that are presented to it by
nodes in the layer beneath it. More specifically,
• At the very lowest layer of the hierarchy nodes receive as input raw data (e.g. pixels of an
image) and continuously construct a belief state that attempts to characterize the sequences
of patterns viewed.
4.3 Emergentist Cognitive Architectures 69
• The second layer, and all those above it, receive as input the belief states of nodes at their
corresponding lower layers, and attempt to construct belief states that capture regularities
in their inputs.
• Each node also receives as input the belief state of the node above it in the hierarchy (which
constitutes “contextual” information)
Fig. 4.6: Small-scale instantiation of the DeSTIN perceptual hierarchy. Each box represents a
node, which corresponds to a spatiotemporal region (nodes higher in the hierarchy corresponding
to larger regions). O denotes the current observation in the region, C is the state of the higherlayer
node, and S and S ′ denote state variables pertaining to two subsequent time steps. In
each node, a statistical learning algorithm is used to predict subsequent states based on prior
states, current observations, and the state of the higher-layer node.
More specifically, each of the DeSTIN nodes, referring to a specific spacetime region, contains
a set of state variables conceived as clusters, each corresponding to a set of previously-observed
sequences of events. These clusters are characterized by centroids (and are hence assumed
roughly spherical in shape), and each of them comprises a certain "spatiotemporal form" recognized
by the system in that region. Each node then contains the task of predicting the likelihood
of a certain centroid being most apropos in the near future, based on the past history of observations
in the node. This prediction may be done by simple probability tabulation, or via
70 4 Brief Survey of Cognitive Architectures
application of supervised learning algorithms such as recurrent neural networks. These clustering
and prediction processes occur separately in each node, but the nodes are linked together
via bidirectional dynamics: each node feeds input to its parents, and receives "advice" from its
parents that is used to condition its probability calculations in a contextual way.
These processes are executed formally by the following basic belief update rule, which governs
the learning process and is identical for every node in the architecture. The belief state is a
probability mass function over the sequences of stimuli that the nodes learns to represent.
Consequently, each node is allocated a predefined number of state variables each denoting a
dynamic pattern, or sequence, that is autonomously learned. The DeSTIN update rule maps
the current observation (o), belief state (b), and the belief state of a higher-layer node or context
(c), to a new (updated) belief state (b ′ ), such that
alternatively expressed as
b ′ (s ′ ) = Pr (s ′ |o, b, c) = Pr (s′ ∩ o ∩ b ∩ c)
, (4.1)
Pr (o ∩ b ∩ c)
b ′ (s ′ ) = Pr(o|s′ , b, c) Pr (s ′ |b, c) Pr (b, c)
. (4.2)
Pr (o|b, c) Pr (b, c)
Under the assumption that observations depend only on the true state, or Pr(o|s ′ , b, c) =
Pr(o|s ′ ), we can further simplify the expression such that
b ′ (s ′ ) = Pr(o|s′ ) Pr (s ′ |b, c)
, (4.3)
Pr (o|b, c)
where Pr (s ′ |b, c) = ∑ Pr (s ′ |s, c) b (s), yielding the belief update rule
s∈S
b ′ (s ′ ) =
Pr (o|s ′ ) ∑ Pr (s ′ |s, c) b (s)
s∈S
∑
Pr (o|s ′′ ) ∑ Pr (s ′′ |s, c) b (s) , (4.4)
s ′′ ∈S
where S denotes the sequence set (i.e. belief dimension) such that the denominator term is a
normalization factor.
One interpretation of eq. (4.4) would be that the static pattern similarity metric, Pr (o|s ′ ) ,
is modulated by a construct that reflects the system dynamics, Pr (s ′ |s, c). As such, the belief
state inherently captures both spatial and temporal information. In our implementation, the
belief state of the parent node, c, is chosen using the selection rule
s∈S
c = arg max b p (s), (4.5)
s
where b p is the belief distribution of the parent node.
A close look at eq. (4.4) reveals that there are two core constructs to be learned, Pr(o|s ′ )
and Pr(s ′ |s, c). In the current DeSTIN design, the former is learned via online clustering while
the latter is learned based on experience by inductively learning a rule that predicts the next
state s ′ given the prior state s and c.
The overall result is a robust framework that autonomously (i.e. with no human engineered
pre-processing of any type) learns to represent complex data patterns, and thus serves the
4.3 Emergentist Cognitive Architectures 71
critical role of building and maintaining a model of the state of the world. In a vision processing
context, for example, it allows for powerful unsupervised classification. If shown a variety of
real-world scenes, it will automatically form internal structures corresponding to the various
natural categories of objects shown in the scenes, such as trees, chairs, people, etc.; and also
the various natural categories of events it sees, such as reaching, pointing, falling. And, as will
be discussed below, it can use feedback from DeSTIN’s action and critic networks to further
shape its internal world-representation based on reinforcement signals.
Benefits of DeSTIN for Perception Processing
DeSTIN’s perceptual network offers multiple key attributes that render it more powerful than
other deep machine learning approaches to sensory data processing:
1. The belief space that is formed across the layers of the perceptual network inherently
captures both spatial and temporal regularities in the data. Given that many applications
require that temporal information be discovered for robust inference, this is a key advantage
over existing schemes.
2. Spatiotemporal regularities in the observations are captured in a coherent manner (rather
than being represented via two separate mechanisms)
3. All processing is both top-down and bottom-up, and both hierarchical and heterarchical,
based on nonlinear feedback connections directing activity and modulating learning in multiple
directions through DeSTIN’s cortical circuits
4. Support for multi-modal fusing is intrinsic within the framework, yielding a powerful state
inference system for real-world, partially-observable settings.
5. Each node is identical, which makes it easy to map the design to massively parallel platforms,
such as graphics processing units.
Points 2-4 in the above list describe how DeSTIN’s perceptual network displays its own
“cognitive synergy” in a way that fits naturally into the overall synergetic dynamics of the overall
CogPrime architecture. Using this cognitive synergy, DeSTIN’s perceptual network addresses
a key aspect of general intelligence: the ability to robustly infer the state of the world, with
which the system interacts, in an accurate and timely manner.
4.3.1.3 DeSTIN for Action and Control
DeSTIN’s perceptual network performs unsupervised world-modeling, which is a critical aspect
of intelligence but of course is not the whole story. DeSTIN’s action network, coupled with the
perceptual network, orchestrates actuator commands into complex movements, but also carries
out other functions that are more cognitive in nature.
For instance, people learn to distinguish between cups and bowls in part via hearing other
people describe some objects as cups and others as bowls. To emulate this kind of learning,
DeSTIN’s critic network provides positive or negative reinforcement signals based on whether
the action network has correctly identified a given object as a cup or a bowl, and this signal
then impacts the nodes in the action network. The critic network takes a simple external “degree
of success or failure” signal and turns it into multiple reinforcement signals to be fed into the
multiple layers of the action network. The result is that the action network self-organizes so
72 4 Brief Survey of Cognitive Architectures
as to include an implicit “cup versus bowl” classifier, whose inputs are the outputs of some of
the nodes in the higher levels of the perceptual network. This classifier belongs in the action
network because it is part of the procedure by which the DeSTIN system carries out the action
of identifying an object as a cup or a bowl.
This example illustrates how the learning of complex concepts and procedures is divided
fluidly between the perceptual network, which builds a model of the world in an unsupervised
way, and the action network, which learns how to respond to the world in a manner that will
receive positive reinforcement from the critic network.
4.3.2 Developmental Robotics Architectures
A particular subset of emergentist cognitive architectures are sufficiently important that we
consider them separately here: these are developmental robotics architectures, focused on controlling
robots without significant “hard-wiring” of knowledge or capabilities, allowing robots
to learn (and learn how to learn, etc.) via their engagement with the world. A significant focus
is often placed here on “intrinsic motivation,” wherein the robot explores the world guided by
internal goals like novelty or curiosity, forming a model of the world as it goes along, based
on the modeling requirements implied by its goals. Many of the foundations of this research
area were laid by Juergen Schmidhuber’s work in the 1990s [Sch91b, Sch91a, Sch95, Sch02], but
now with more powerful computers and robots the area is leading to more impressive practical
demonstrations.
We mention here a handful of the important initiatives in this area:
• Juyang Weng’s Dav [HZT + 02] and SAIL [WHZ + 00] projects involve mobile robots that
explore their environments autonomously, and learn to carry out simple tasks by building up
their own world-representations through both unsupervised and teacher-driven processing
of high-dimensional sensorimotor data. The underlying philosophy is based on human child
development [WH06], the knowledge representations involved are neural network based,
and a number of novel learning algorithms are involved, especially in the area of vision
processing.
• FLOWERS [BO09], an initiative at the French research institute INRIA, led by Pierre-
Yves Oudeyer, is also based on a principle of trying to reconstruct the processes of development
of the human child’s mind, spontaneously driven by intrinsic motivations. Kaplan
[Kap08] has taken this project in a direction closely related to our own via the creation
of a “robot playroom.” Experiential language learning has also been a focus of the project
[OK06], driven by innovations in speech understanding.
• IM-CLEVER 1 , a new European project coordinated by Gianluca Baldassarre and conducted
by a large team of researchers at different institutions, is focused on creating software
enabling an iCub [MSV + 08] humanoid robot to explore the environment and learn to carry
out human childlike behaviors based on its own intrinsic motivations. As this project is the
closest to our own we will discuss it in more depth below.
Like CogPrime, IM-CLEVER is a humanoid robot intelligence architecture guided by intrinsic
motivations, and using hierarchical architectures for reinforcement learning and sensory ab-
1 http://im-clever.noze.it/project/project-description
4.4 Hybrid Cognitive Architectures 73
straction. IM-CLEVER’s motivational structure is based in part on Schmidhuber’s informationtheoretic
model of curiosity [Sch06]; and CogPrime’s Psi-based motivational structure utilizes
probabilistic measures of novelty, which are mathematically related to Schmidhuber’s measures.
On the other hand, IM-CLEVER’s use of reinforcement learning follows Schmidhuber’s
earlier work RL for cognitive robotics [BS04, BZGS06], Barto’s work on intrinsically motivated
reinforcement learning [SB06, SM05], and Lee’s [LMC07b, LMC07a] work on developmental
reinforcement learning; whereas CogPrime’s assemblage of learning algorithms is more diverse,
including probabilistic logic, concept blending and other symbolic methods (in the OCP component)
as well as more conventional reinforcement learning methods (in the DeSTIN component).
In many respects IM-CLEVER bears a moderately strong resemblance to DeSTIN, whose
integration with CogPrime is discussed in Chapter 26 of Part 2 (although IM-CLEVER has
much more focus on biological realism than DeSTIN). Apart from numerous technical differences,
the really big distinction between IM-CLEVER and CogPrime is that in the latter we
are proposing to hybridize a hierarchical-abstraction/reinforcement-learning system (such as
DeSTIN) with a more abstract symbolic cognition engine that explicitly handles probabilistic
logic and language. IM-CLEVER lacks the aspect of hybridization with a symbolic system, taking
more of a pure emergentist strategy. Like DeSTIN considered as a standalone architecture
IM-CLEVER does entail a high degree of cognitive synergy, between components dealing with
perception, world-modeling, action and motivation. However, the “emergentist versus hybrid”
is a large qualitative difference between the two approaches.
In all, while we largely agree with the philosophy underlying developmental robotics, our
intuition is that the learning and representational mechanisms underlying the current systems
in this area are probably not powerful enough to lead to human child level intelligence. We
expect that these systems will develop interesting behaviors but fall short of robust preschool
level competency, especially in areas like language and reasoning where symbolic systems have
typically proved more effective. This intuition is what impels us to pursue a hybrid approach,
such as CogPrime. But we do feel that eventually, once the mechanisms underlying brains are
better understood and robotic bodies are richer in sensation and more adept in actuation, some
sort of emergentist, developmental-robotics approach can be successful at creating humanlike,
human-level AGI.
4.4 Hybrid Cognitive Architectures
In response to the complementary strengths and weaknesses of the symbolic and emergentist
approaches, in recent years a number of researchers have turned to integrative, hybrid architectures,
which combine subsystems operating according to the two different paradigms. The
combination may be done in many different ways, e.g. connection of a large symbolic subsystem
with a large subsymbolic system, or the creation of a population of small agents each of which
is both symbolic and subsymbolic in nature.
Nils Nilsson expressed the motivation for hybrid AGI systems very clearly in his article at
the AI-50 conference (which celebrated the 50’th anniversary of the AI field) [Nil09]. While
affirming the value of the Physical Symbol System Hypothesis that underlies symbolic AI, he
argues that “the PSSH explicitly assumes that, whenever necessary, symbols will be grounded
in objects in the environment through the perceptual and effector capabilities of a physical
symbol system.” Thus, he continues,
74 4 Brief Survey of Cognitive Architectures
“I grant the need for non-symbolic processes in some intelligent systems, but I think they supplement
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 reasoning
on “explicit knowledge” with a connectionist component for managing “implicit knowledge.”
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 perception,
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 motivation,
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 comprehensiveness
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 advanced
processes like language and abstract reasoning.
• PolyScheme [Cas07] integrates multiple methods of representation, reasoning and inference
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 reasoning
including object identity, events, causality, and spatial relations. The integration of
4.4 Hybrid Cognitive Architectures 75
reasoning methods is powerful, but the overall cognitive architecture is simplistic compared
to other systems and seems focused more on problem-solving than on the broader problem
of intelligent agent control.
• Shruti [SA93] is a fascinating biologically-inspired model of human reflexive inference,
which represents in connectionist architecture relations, types, entities and causal rules
using focal-clusters. However, much like Hofstadter’s earlier Copycat architecture [Hof95],
Shruti seems more interesting as a prototype exploration of ideas than as a practical AGI
system; at least, after a significant time of development it has not proved significantly
effective in any applications
• James Albus’s 4D/RCS robotics architecture shares a great deal with some of the emergentist
architectures discussed above, e.g. it has the same hierarchical pattern recognition
structure as DeSTIN and HTM, and the same three cross-connected hierarchies as DeSTIN,
and shares with the developmental robotics architectures a focus on real-time adaptation to
the structure of the world. However, 4D/RCS is not foundationally learning-based but relies
on hard-wired architecture and algorithms, intended to mimic the qualitative structure of
relevant parts of the brain (and intended to be augmented by learning, which differentiates
it from emergentist approaches.
As our own CogPrime approach is a hybrid architecture, it will come as no surprise that
we believe several of the existing hybrid architectures are fundamentally going in the right
direction. However, nearly all the existing hybrid architectures have severe shortcomings which
we feel will prevent them from achieving robust humanlike AGI.
Many of the hybrid architectures are in essence “multiple, disparate algorithms carrying out
separate functions, encapsulated in black boxes and communicating results with each other.”
For instance, PolyScheme, ACT-R and CLARION all display this “modularity” property to a
significant extent. These architectures lack the rich, real-time interaction between the internal
dynamics of various memory and learning processes that we believe is critical to achieving
humanlike general intelligence using realistic computational resources. On the other hand, those
architectures that feature richer integration – such as DUAL, Shruti, LIDA and MicroPsi – have
the flaw of relying (at least in their current versions) on overly simplistic learning algorithms,
which drastically limits their scalability.
It does seem plausible to us that some of these hybrid architectures could be dramatically
extended or modified so as to produce humanlike general intelligence. For instance, one could
replace LIDA’s learning algorithms with others that interrelate with each other in a nuanced
synergetic way; or one could replace MicroPsi’s simple learning and reasoning methods with
much more powerful and scalable ones acting on the same data structures. However, making
these changes would dramatically alter the cognitive architectures in question on multiple levels.
4.4.1 Neural versus Symbolic; Global versus Local
The “symbolic versus emergentist” dichotomy that we have used to structure our review of cognitive
architectures is not absolute nor fully precisely defined; it is more of a heuristic distinction.
In this section, before plunging into the details of particular hybrid cognitive architectures, we
review two other related dichotomies that are useful for understanding hybrid systems: neural
versus symbolic systems, and globalist versus localist knowledge representation.
76 4 Brief Survey of Cognitive Architectures
4.4.1.1 Neural-Symbolic Integration
The distinction between neural and symbolic systems has gotten fuzzier and fuzzier in recent
years, with developments such as
• Logic-based systems being used to control embodied agents (hence using logical terms to
deal with data that is apparently perception or actuation-oriented in nature, rather than
being symbolic in the semiotic sense), see [SS03a] and [GMIH08].
• Hybrid systems combining neural net and logical parts, or using logical or neural net components
interchangeably in the same role [LAon].
• Neural net systems being used for strongly symbolic tasks such as automated grammar
learning ([Elm91], [Elm91], plus more recent work.)
Figure 4.7 presents a schematic diagram of a generic neural-symbolic system, generalizing
from [BH05], a paper that gives an elegant categorization of neural-symbolic AI systems. Figure
4.8 depicts several broad categories of neural-symbolic architecture.
Fig. 4.7: Generic neural-symbolic architecture
Bader and Hitzler categorize neural-symbolic systems according to three orthogonal axes:
interrelation, language and usage. “Language” refers to the type of language used in the symbolic
component, which may be logical, automata-based, formal grammar-based, etc. “Usage” refers
to the purpose to which the neural-symbolic interrelation is put. We tend to use “learning” as
an encompassing term for all forms of ongoing knowledge-creation, whereas Bader and Hitzler
distinguish learning from reasoning.
Of Bader and Hitzler’s three axes the one that interests us most here is “interrelation”, which
refers to the way the neural and symbolic components of the architecture intersect with each
other. They distinguish “hybrid” architectures which contain separate but equal, interacting
neural and symbolic components; versus “integrative” architectures in which the symbolic component
essentially rides piggyback on the neural component, extracting information from it and
helping it carry out its learning, but playing a clearly derived and secondary role. We prefer
Sun’s (2001) term “monolithic” to Bader and Hitzler’s “integrative” to describe this type of
system, as the latter term seems best preserved in its broader meaning.
4.4 Hybrid Cognitive Architectures 77
Fig. 4.8: Broad categories of neural-symbolic architecture
Within the scope of hybrid neural-symbolic systems, there is another axis which Bader and
Hitzler do not focus on, because the main interest of their review is in monolithic systems. We
call this axis "interactivity"’, and what we are referring to is the frequency of high-informationcontent,
high-influence interaction between the neural and symbolic components in the hybrid
system. In a low-interaction hybrid system, the neural and symbolic components don’t exchange
large amounts of mutually influential information all that frequently, and basically act like
independent system components that do their learning/reasoning/thinking periodically sending
each other their conclusions. In some cases, interaction may be asymmetric: one component may
frequently send a lot of influential information to the other, but not vice versa. However, our
hypothesis is that the most capable neural-symbolic systems are going to be the symmetrically
highly interactive ones.
In a symmetric high-interaction hybrid neural-symbolic system, the neural and symbolic
components exchange influential information sufficiently frequently that each one plays a major
role in the other one’s learning/reasoning/thinking processes. Thus, the learning processes of
each component must be considered as part of the overall dynamic of the hybrid system. The
two components aren’t just feeding their outputs to each other as inputs, they’re mutually
guiding each others’ internal processing.
One can make a speculative argument for the relevance of this kind of architecture to neuroscience.
It seems plausible that this kind of neural-symbolic system roughly emulates the kind
of interaction that exists between the brain’s neural subsystems implementing localist symbolic
processing, and the brain’s neural subsystems implementing globalist, classically “connectionist”
processing. It seems most likely that, in the brain, symbolic functionality emerges from
an underlying layer of neural dynamics. However, it is also reasonable to conjecture that this
symbolic functionality is confined to a functionally distinct subsystem of the brain, which then
78 4 Brief Survey of Cognitive Architectures
interacts with other subsystems in the brain much in the manner that the symbolic and neural
components of a symmetric high-interaction neural-symbolic system interact.
Neuroscience speculations aside, however, our key conjecture regarding neural-symbolic integration
is that this sort of neural-symbolic system presents a promising direction for artificial
general intelligence research. In Chapter 26 of Volume 2 we will give a more concrete idea of
what a symmetric high-interaction hybrid neural-symbolic architecture might look like, exploring
the potential for this sort of hybridization between the OpenCogPrime AGI architecture
(which is heavily symbolic in nature) and hierarchical attractor neural net based architectures
such as DeSTIN.
4.5 Globalist versus Localist Representations
Another interesting distinction, related to but different from “symbolic versus emergentist”
and “neural versus symbolic”, may be drawn between cognitive systems (or subsystems) where
memory is essentially global, and those where memory is essentially local. In this section
we will pursue this distinction in various guises, along with the less familiar notion of glocal
memory.
This globalist/localist distinction is most easily conceptualized by reference to memories
corresponding to categories of entities or events in an external environment. In an AI system
that has an internal notion of “activation” – i.e. in which some of its internal elements are more
active than others, at any given point in time – one can define the internal image of an external
event or entity as the fuzzy set of internal elements that tend to be active when that event or
entity is presented to the system’s sensors. If one has a particular set S of external entities or
events of interest, then, the degree of memory localization of such an AI system relative to S
may be conceived as the percentage of the system’s internal elements that have a high degree
of membership in the internal image of an average element of S.
Of course, this characterization of localization has its limitations, such as the possibility of
ambiguity regarding what are the “system elements” of a given AI system; and the exclusive
focus on internal images of external phenomena rather than representation of internal abstract
concepts. However, our goal here is not to formulate an ultimate, rigorous and thorough ontology
of memory systems, but only to pose a “rough and ready” categorization so as to properly frame
our discussion of some specific AGI issues relevant to CogPrime. Clearly the ideas pursued here
will benefit from further theoretical exploration and elaboration.
In this sense, a Hopfield neural net [Ami89] would be considered “globalist” since it has a low
degree of memory localization (most internal images heavily involve a large number of system
elements); whereas Cyc would be considered “localist” as it has a very high degree of memory
localization (most internal images are heavily focused on a small set of system elements).
However, although Hopfield nets and Cyc form handy examples, the “globalist vs. localist”
distinction as described above is not identical to the “neural vs. symbolic” distinction. For it is
in principle quite possible to create localist systems using formal neurons, and also to create
globalist systems using formal logic. And “globalist-localist” is not quite identical to “symbolic vs
emergentist” either, because the latter is about coordinated system dynamics and behavior not
just about knowledge representation. CogPrime combines both symbolic and (loosely) neural
representations, and also combines globalist and localist representations in a way that we will
call “glocal” and analyze more deeply in Chapter 13; but there are many other ways these various
4.5 Globalist versus Localist Representations 79
properties could be manifested by AI systems. Rigorously studying the corpus of existing (or
hypothetical!) cognitive architectures using these ideas would be a large task, which we do not
undertake here.
In the next sections we review several hybrid architectures in more detail, focusing most
deeply on LIDA and MicroPsi which have been directly inspirational for CogPrime.
4.5.1 CLARION
Ron Sun’s CLARION architecture (see Figure 4.9) is interesting in its combination of symbolic
and neural aspects – a combination that is used in a sophisticated way to embody the distinction
and interaction between implicit and explicit mental processes. From a CLARION perspective,
architectures like Soar and ACT-R are severely limited in that they deal only with explicit
knowledge and associated learning processes.
CLARION consists of a number of distinct subsystems, each of which contains a dual representational
structure, including a “rules and chunks” symbolic knowledge store somewhat
similar to ACT-R, and a neural net knowledge store embodying implicit knowledge. The main
subsystems are:
• An action-centered subsystem to control actions;
• A non-action-centered subsystem to maintain general knowledge;
• A motivational subsystem to provide underlying motivations for perception, action, and
cognition;
• A meta-cognitive subsystem to monitor, direct, and modify the operations of all the other
subsystems.
Fig. 4.9: The CLARION cognitive architecture.
80 4 Brief Survey of Cognitive Architectures
4.5.2 The Society of Mind and the Emotion Machine
In his influential but controversial book The Society of Mind [Min88], Marvin Minsky described
a model of human intelligence as something that is built up from the interactions of numerous
simple agents. He spells out in great detail how various particular cognitive functions may be
achieved via agents and their interactions. He leaves no room for any central algorithms or
structures of thought, famously arguing: “What magical trick makes us intelligent? The trick
is that there is no trick. The power of intelligence stems from our vast diversity, not from any
single, perfect principle.”
This perspective was extended in the more recent work The Emotion Machine [Min07], where
Minsky argued that emotions are “ways to think” evolved to handle different “problem types”
that exist in the world. The brain is posited to have rule-based mechanisms (selectors) that
turns on emotions to deal with various problems.
Overall, both of these works serve better as works of speculative cognitive science than as
works of AI or cognitive architecture per se. As neurologist Richard Restak said in his review
of Emotion Machine, “Minsky does a marvelous job parsing other complicated mental activities
into simpler elements. ... But he is less effective in relating these emotional functions to what’s
going on in the brain.” As Restak added, he is also not so effective at relating these emotional
functions to straightforwardly implementable algorithms or data structures.
Push Singh, in his PhD thesis and followup work [SBC05], did the best job so far of creating
a concrete AI design based on Minsky’s ideas. While Singh’s system was certainly interesting,
it was also noteworthy for its lack of any learning mechanisms, and its exclusive focus on
explicit rather than implicit knowledge. Due to Singh’s tragic death, his work was never brought
anywhere near completion. It seems fair to say that there has not yet been a serious cognitive
architecture posed based closely on Minsky’s ideas.
4.5.3 DUAL
The closest thing to a Minsky-ish cognitive architecture is probably DUAL, which takes the
Society of Mind concept and adds to it a number of other interesting ideas. DUAL integrates
symbolic and connectionist approaches at a deeper level than CLARION, and has been used
to model various cognitive functions such as perception, analogy and judgment. Computations
in DUAL emerge from the self-organized interaction of many micro-agents, each of which is
a hybrid symbolic/connectionist device. Each DUAL agent plays the role of a neural network
node, with an activation level and activation spreading dynamics; but also plays the role of
a symbol, manipulated using formal rules. The agents exchange messages and activation via
links that can be learned and modified, and they form coalitions which collectively represent
concepts, episodes, and facts.
The structure of the model is sketchily depicted in Figure 4.10, which covers the application
of DUAL to a toy environment called TextWorld. The visual input corresponding to a stimulus
is presented on a two-dimensional visual array representing the front end of the system.
Perceptual primitives like blobs and terminations are immediately generated by cheap parallel
computations. Attention is controlled at each time by an object which allocates it selectively
to some area of the stimulus. A detailed symbolic representation is constructed for this area
which tends to fade away as attention is withdrawn from it and allocated to another one. Cate-
4.5 Globalist versus Localist Representations 81
gorization of visual memory contents takes place by retrieving object and scene categories from
DUAL’s semantic memory and mapping them onto current visual memory representations.
Fig. 4.10: The three main components of the DUAL model: the retinotopic visual array (RVA),
the visual working memory (VWM) and DUAL’s semantic memory. Attention is allocated to
an area of the visual array by the object in VWM controlling attention, while scene and object
categories corresponding to the contents of VWM are retrieved from the semantic memory.
In principle the DUAL framework seems quite powerful; using the language of CogPrime,
however, it seems to us that the learning mechanisms of DUAL have not been formulated in
such a way as to give rise to powerful, scalable cognitive synergy. It would likely be possible
to create very powerful AGI systems within DUAL, and perhaps some very CogPrime -like
systems as well. But the systems that have been created or designed for use within DUAL so
far seem not to be that powerful in their potential or scope.
4.5.4 4D/RCS
In a rather different direction, James Albus, while at the National Bureau of Standards, developed
a very thorough and impressive architecture for intelligent robotics called 4D/RCS,
which was implemented in a number of machines including unmanned automated vehicles. This
architecture lacks critical aspects of intelligence such as learning and creativity, but combines
perception, action, planning and world-modeling in a highly effective and tightly-integrated
fashion.
The architecture has three hierarchies of memory/processing units: one for perception, one
for action and one for modeling and guidance. Each unit has a certain spatiotemporal scope,
82 4 Brief Survey of Cognitive Architectures
and (except for the lowest level) supervenes over children whose spatiotemporal scope is a subset
of its own. The action hierarchy takes care of decomposing tasks into subtasks; whereas the
sensation hierarchy takes care of grouping signals into entities and events. The modeling/guidance
hierarchy mediates interactions between perception and action based on its understanding
of the world and the system’s goals.
In his book [AM01] Albus describes methods for extending 4D/RCS into a complete cognitive
architecture, but these extensions have not been elaborated in full detail nor implemented.
Fig. 4.11: Albus’s 4D-RCS architecture for a single vehicle
4.5.5 PolyScheme
Nick Cassimatis’s PolyScheme architecture [Cas07] shares with GLAIR the use of multiple
logical reasoning methods on a common knowledge store. While its underlying ideas are quite
general, currently PolyScheme is being developed in the context of the “object tracking” domain
(construed very broadly). As a logic framework PolyScheme is fairly conventional (unlike GLAIR
or NARS with their novel underlying formalisms), but PolyScheme has some unique conceptual
aspects, for instance its connection with Cassimatis’s theory of mind, which holds that the same
core set of logical concepts and relationships underlies both language and physical reasoning
[Cas04]. This ties in with the use of a common knowledge store for multiple cognitive processes;
for instance it suggests that
• the same core relationships can be used for physical reasoning and parsing, but that each
of these domains may involve some additional relationships.
• language processing may be done via physical-reasoning-based cognitive processes, plus the
additional activity of some language-specific processes
4.5 Globalist versus Localist Representations 83
Fig. 4.12: Albus’s perceptual, motor and modeling hierarchies
4.5.6 Joshua Blue
Sam Adams and his colleagues at IBM have created a cognitive architecture called Joshua Blue
[AABL02], which has some significant similarities to CogPrime. Similar to our current research
direction with CogPrime, Joshua Blue was created with loose emulation of child cognitive
development in mind; and, also similar to CogPrime, it features a number of cognitive processes
acting on a common neural-symbolic knowledge store. The specific cognitive processes involved
in Joshua Blue and CogPrime are not particularly similar, however. At time of writing (2012)
84 4 Brief Survey of Cognitive Architectures
Joshua Blue is not under active development and has not been for some time; however, the
project may be reanimated in future.
Joshua Blue’s core knowledge representation is a semantic network of nodes connected by
links along which activation spreads. Although many of the nodes have specific semantic referents,
as in a classical semantic net, the spread of activation through the network is designed to
lead to the emergence of “assemblies” (which could also be thought of as dynamical attractors)
in a manner more similar to an attractor neural network.
A major difference from typical semantic or neural network models is the central role that
affect plays in the system’s dynamics. The weights of the links in the knowledge base are adjusted
dynamically based on the emotional context – a very direct way of ensuring that cognitive
processes and mental representations are continuously influenced by affect. Qualitatively, this
mimics the way that particular emotions in the human brain correlate with the dissemination
throughout the brain of particular neurotransmitters, which then affect synaptic activity.
A result of this architecture is that in Joshua Blue, emotion directs attention in a very direct
way: affective weighting is important in determining which associated objects will become part of
the focus of attention, or will be retained from memory. A notable similarity between CogPrime
and Joshua Blue is that in both systems, nodes are assigned two quantitative attention values,
one governing allocation of current system resources (mainly processor time; this is CogPrime’s
ShortTermImportance) and one governing the long-term allocation of memory (CogPrime’s
LongTermImportance).
The concrete work done with Joshua Blue involved using it to control a simple agent in a simulated
world, with the goal that via human interaction, the agent would develop a complex and
humanlike emotional and motivational structure from its simple in-built emotions and drives,
and would then develop complex cognitive capabilities as part of this development process.
4.5.7 LIDA
The LIDA architecture developed by Stan Franklin and his colleagues [BF09] is based on the
concept of the “cognitive cycle” - a notion that is important to nearly every BICA (Biologically
Inspired Cognitive Architectures) and also to the brain, but that plays a particularly central
role in LIDA. As Franklin says, "as a matter of principle, every autonomous agent, be it human,
animal, or artificial, must frequently sample (sense) its environment, process (make sense of)
this input, and select an appropriate response (action). The agent’s “life” can be viewed as
consisting of a continual sequence of iterations of these cognitive cycles. Such cycles constitute
the indivisible elements of attention, the least sensing and acting to which we can attend. A
cognitive cycle can be thought of as a moment of cognition, a cognitive "moment"."
4.5.8 The Global Workspace
LIDA is heavily based on the “global workspace” concept developed by Bernard Baars. As this
concept is also directly relevant to CogPrime it is worth briefly describing here.
In essence Baars’ Global Workspace Theory (GWT) is a particular hypothesis about how
working memory works and the role it plays in the mind. Baars conceives working memory as the
4.5 Globalist versus Localist Representations 85
“inner domain in which we can rehearse telephone numbers to ourselves or, more interestingly,
in which we carry on the narrative of our lives. It is usually thought to include inner speech
and visual imagery.” Baars uses the term “consciousness” to refer to the contents of working
memory – a theoretical commitment that is not part of the CogPrime design. In this section
we will use the term “consciousness” in Baars’ way, but not throughout the rest of the book.
Baars conceives working memory and consciousness in terms of a “theater metaphor” – according
to which, in the “theater of consciousness” a “spotlight of selective attention” shines
a bright spot on stage. The bright spot reveals the global workspace – the contents of consciousness,
which may be metaphorically considered as a group of actors moving in and out of
consciousness, making speeches or interacting with each other. The unconscious is represented
by the audience watching the play ... and there is also a role for the director (the mind’s executive
processes) behind the scenes, along with a variety of helpers like stage hands, script
writers, scene designers, etc.
GWT describes a fleeting memory with a duration of a few seconds. This is much shorter
than the 10-30 seconds of classical working memory – according to GWT there is a very brief
“cognitive cycle” in which the global workspace is refreshed, and the time period an item remains
in working memory generally spans a large number of these elementary “refresh” actions. GWT
contents are proposed to correspond to what we are conscious of, and are said to be broadcast
to a multitude of unconscious cognitive brain processes. Unconscious processes, operating in
parallel, can form coalitions which can act as input processes to the global workspace. Each
unconscious process is viewed as relating to certain goals, and seeking to get involved with
coalitions that will get enough importance to become part of the global workspace – because
once they’re in the global workspace they’ll be allowed to broadcast out across the mind as a
whole, which include broadcasting to the internal and external actuators that allow the mind
to do things. Getting into the global workspace is a process’s best shot at achieving its goals.
Obviously, the theater metaphor used to describe the GWT is evocative but limited; for
instance, the unconscious in the mind does a lot more than the audience in a theater. The
unconscious comes up with complex creative ideas sometimes, which feed into consciousness –
almost as if the audience is also the scriptwriter. Baars’ theory, with its understanding of unconscious
dynamics in terms of coalition-building, fails to describe the subtle dynamics occurring
within the various forms of long-term memory, which result in subtle nonlinear interactions
between long term memory and working memory. But nevertheless, GWT successfully models
a number of characteristics of consciousness, including its role in handling novel situations, its
limited capacity, its sequential nature, and its ability to trigger a vast range of unconscious
brain processes. It is the framework on which LIDA’s theory of the cognitive cycle is built.
4.5.9 The LIDA Cognitive Cycle
The simplest cognitive cycle is that of an animal, which senses the world, compares sensation to
memory, and chooses an action, all in one fluid subjective moment. But the same cognitive cycle
structure/process applies to higher-level cognitive processes as well. The LIDA architecture is
based on the LIDA model of the cognitive cycle, which posits a particular structure underlying
the cognitive cycle that possess the generality to encompass both simple and complex cognitive
moments.
86 4 Brief Survey of Cognitive Architectures
The LIDA cognitive cycle itself is a theoretical construct that can be implemented in many
ways, and indeed other BICAs like CogPrime and Psi also manifest the LIDA cognitive cycle
in their dynamics, though utilizing different particular structures to do so.
Figure 4.13 shows the cycle pictorially, starting in the upper left corner and proceeding
clockwise. At the start of a cycle, the LIDA agent perceives its current situation and allocates
attention differentially to various parts of it. It then broadcasts information about the most
important parts (which constitute the agent’s consciousness), and this information gets features
extracted from it, when then get passed along to episodic and semantic memory, that interact
in the “global workspace” to create a model for the agent’s current situation. This model then,
in interaction with procedural memory, enables the agent to choose an appropriate action and
execute it - the critical “action-selection” phase!
Fig. 4.13: The LIDA Cognitive Cycle
The LIDA Cognitive Cycle in More Depth
2
We now run through the cognitive cycle in more detail. It begins with sensory stimuli from
the agent’s external internal environment. Low-level feature detectors in sensory memory begin
the process of making sense of the incoming stimuli. These low-level features are passed to
perceptual memory where higher-level features, objects, categories, relations, actions, situations,
2 This section paraphrases heavily from [Fra06]
4.5 Globalist versus Localist Representations 87
etc. are recognized. These recognized entities, called percepts, are passed to the workspace,
where a model of the agent’s current situation is assembled.
Workspace structures serve as cues to the two forms of episodic memory, yielding both short
and long term remembered local associations. In addition to the current percept, the workspace
contains recent percepts that haven’t yet decayed away, and the agent’s model of the thencurrent
situation previously assembled from them. The model of the agent’s current situation is
updated from the previous model using the remaining percepts and associations. This updating
process will typically require looking back to perceptual memory and even to sensory memory,
to enable the understanding of relations and situations. This assembled new model constitutes
the agent’s understanding of its current situation within its world. Via constructing the model,
the agent has made sense of the incoming stimuli.
Now attention allocation comes into play, because a real agent lacks the computational resources
to work with all parts of its world-model with maximal mental focus. Portions of the
model compete for attention. These competing portions take the form of (potentially overlapping)
coalitions of structures comprising parts the model. Once one such coalition wins the
competition, the agent has decided what to focus its attention on.
And now comes the purpose of all this processing: to help the agent to decide what to do
next. The winning coalition passes to the global workspace, the namesake of Global Workspace
Theory, from which it is broadcast globally. Though the contents of this conscious broadcast
are available globally, the primary recipient is procedural memory, which stores templates of
possible actions including their context and possible results.
Procedural memory also stores an activation value for each such template – a value that
attempts to measure the likelihood of an action taken within its context producing the expected
result. It’s worth noting that LIDA makes a rather specific assumption here. LIDA’s
“activation” values are like the probabilistic truth values of the implications in CogPrime’s
Context ∧ Procedure → Goal triples. However, in CogPrime this probability is not the same as
the ShortTermImportance “attention value” associated with the Implication link representing
that implication. Here LIDA merges together two concepts that in CogPrime are separate.
Templates whose contexts intersect sufficiently with the contents of the conscious broadcast
instantiate copies of themselves with their variables specified to the current situation. These
instantiations are passed to the action selection mechanism, which chooses a single action from
these instantiations and those remaining from previous cycles. The chosen action then goes to
sensorimotor memory, where it picks up the appropriate algorithm by which it is then executed.
The action so taken affects the environment, and the cycle is complete.
The LIDA model hypothesizes that all human cognitive processing is via a continuing iteration
of such cognitive cycles. It acknowledges that other cognitive processes may also occur,
refining and building on the knowledge used in the cognitive cycle (for instance, the cognitive
cycle itself doesn’t mention abstract reasoning or creativity). But the idea is that these other
processes occur in the context of the cognitive cycle, which is the main loop driving the internal
and external activities of the organism.
4.5.9.1 Avoiding Combinatorial Explosion via Adaptive Attention Allocation
LIDA avoids combinatorial explosions in its inference processes via two methods, both of which
are also important in CogPrime :
• combining reasoning via association with reasoning via deduction
88 4 Brief Survey of Cognitive Architectures
• foundational use of uncertainty in reasoning
One can create an analogy between LIDA’s workspace structures and codelets and a logicbased
architecture’s assertions and functions. However, LIDA’s codelets only operate on the
structures that are active in the workspace during any given cycle. This includes recent perceptions,
their closest matches in other types of memory, and structures recently created by other
codelets. The results with the highest estimate of success, i.e. activation, will then be selected.
Uncertainty plays a role in LIDA’s reasoning in several ways, most notably through the base
activation of its behavior codelets, which depend on the model’s estimated probability of the
codelet’s success if triggered. LIDA observes the results of its behaviors and updates the base
activation of the responsible codelets dynamically.
We note that for this kind of uncertain inference/activation interplay to scale well, some
level of cognitive synergy must be present; and based on our understanding of LIDA it is not
clear to us whether the particular inference and association algorithms used in LIDA possess
the requisite synergy.
4.5.9.2 LIDA versus CogPrime
The LIDA cognitive cycle, broadly construed, exists in CogPrime as in other cognitive architectures.
To see how, it suffices to map the key LIDA structures into corresponding CogPrime
structures, as is done in Table 4.1. Of course this table does not cover all CogPrime processes,
as LIDA does not constitute a thorough explanation of CogPrime structure and dynamics. And
in most cases the corresponding CogPrime and LIDA processes don’t work in exactly the same
way; for instance, as noted above, LIDA’s action selection relies solely on LIDA’s “activation”
values, whereas CogPrime’s action selection process is more complex, relying on aspects of
CogPrime that lack LIDA analogues.
4.5.10 Psi and MicroPsi
We have saved for last the architecture that has the most in common with CogPrime : Joscha
Bach’s MicroPsi architecture, closely based on Dietrich Dorner’s Psi theory. CogPrime has
borrowed substantially from Psi in its handling of emotion and motivation; but Psi also has
other aspects that differ considerably from CogPrime. Here we will focus more heavily on the
points of overlap, but will mention the key points of difference as well.
The overall Psi cognitive architecture, which is centered on the Psi model of the motivational
system, is roughly depicted in Figure 4.14.
Psi’s motivational system begins with Demands, which are the basic factors that motivate
the agent. For an animal these would include things like food, water, sex, novelty, socialization,
protection of one’s children, and so forth. For an intelligent robot they might include things
like electrical power, novelty, certainty, socialization, well-being of others and mental growth.
Psi also specifies two fairly abstract demands and posits them as psychologically fundamental
(see Figure 4.15):
• competence, the effectiveness of the agent at fulfilling its Urges
• certainty, the confidence of the agent’s knowledge
4.5 Globalist versus Localist Representations 89
LIDA
CogPrime
Declarative memory Atomspace
attentional codelets Schema that adjust importance of Atoms explicitly
coalitions
maps
global workspace
attentional focus
behavior codelets
schema
procedural memory (scheme net) procedures in ProcedureRepository; and network of
SchemaNodes in the Atomspace
action selection (behavior net) propagation of STICurrency from goals to actions, and
action selection process
transient episodic memory perceptual atoms entering AT with high STI, which
rapidly decreases in most cases
local workspaces
bubbles of interlinked Atoms with moderate importance,
focused on by a subset of MindAgents (defined
in Chapter 19 of Part 2) for a period of time
perceptual associative memory HebbianLinks in the AT
sensory memory
spaceserver/timeserver, plus auxiliary stores for other
senses
sensorimotor memory Atoms storing record of actions taken, linked in with
Atoms indexed in sensory memory
Table 4.1: CogPrime Analogues of Key LIDA Features
Each demand is assumed to come with a certain “target level” or “target range” (and these
may fluctuate over time, or may change as a system matures and develops). An Urge is said to
develop when a demand deviates from its target range: the urge then seeks to return the demand
to its target range. For instance, in an animal-like agent the demand related to food is more
clearly described as “fullness,” and there is a target range indicating that the agent is neither too
hungry nor too full of food. If the agent’s fullness deviates from this range, an Urge to return
the demand to its target range arises. Similarly, if an agent’s novelty deviates from its target
range, this means the agent’s life has gotten either too boring or too disconcertingly weird, and
the agent gets an Urge for either more interesting activities (in the case of below-range novelty)
or more familiar ones (in the case of above-range novelty).
There is also a primitive notion of Pleasure (and its opposite, displeasure), which is considered
as different from the complex emotion of “happiness.” Pleasure is understood as associated
with Urges: pleasure occurs when an Urge is (at least partially) satisfied, whereas displeasure
occurs when an urge gets increasingly severe. The degree to which an Urge is satisfied is not
necessarily defined instantaneously; it may be defined, for instance, as a time-decaying weighted
average of the proximity of the demand to its target range over the recent past.
So, for instance if an agent is bored and gets a lot of novel stimulation, then it experiences
some pleasure. If it’s bored and then the monotony of its stimulation gets even more extreme,
then it experiences some displeasure.
Note that, according to this relatively simplistic approach, any decrease in the amount of
dissatisfaction causes some pleasure; whereas if everything always continues within its acceptable
range, there isn’t any pleasure. This may seem a little counterintuitive, but it’s important
to understand that these simple definitions of “pleasure” and “displeasure” are not intended to
fully capture the natural language concepts associated with those words. The natural language
terms are used here simply as heuristics to convey the general character of the processes in-
90 4 Brief Survey of Cognitive Architectures
Fig. 4.14: High-Level Architecture of the Psi Model
volved. These are very low level processes whose analogues in human experience are largely
below the conscious level.
A Goal is considered as a statement that the system may strive to make true at some future
time. A Motive is an (urge, goal) pair, consisting of a goal whose satisfaction is predicted to
imply the satisfaction of some urge. In fact one may consider Urges as top-level goals, and the
agent’s other goals as their subgoals.
In Psi an agent has one “ruling motive” at any point in time, but this seems an oversimplification
more applicable to simple animals than to human-like or other advanced AI systems.
In general one may think of different motives having different weights indicating the amount of
resources that will be spent on pursuing them.
Emotions in Psi are considered as complex systemic response-patterns rather than explicitly
constructed entities. An emotion is the set of mental entities activated in response to a certain
set of urges. Dorner conceived theories about how various common emotions emerge from the
dynamics of urges and motives as described in the Psi model. “Intentions” are also considered as
composite entities: an intention at a given point in time consists of the active motives, together
with their related goals, behavior programs and so forth.
4.5 Globalist versus Localist Representations 91
The basic logic of action in Psi is carried out by “triples” that are very similar to CogPrime’s
Context ∧ Procedure → Goal triples. However, an important role is played by four modulators
that control how the processes of perception, cognition and action selection are regulated at a
given time:
• activation, which determines the degree to which the agent is focused on rapid, intensive
activity versus reflective, cognitive activity
• resolution level, which determines how accurately the system tries to perceive the world
• certainty, which determines how hard the system tries to achieve definite, certain knowledge
• selection threshold, which determines how willing the system is to change its choice of which
goals to focus on
These modulators characterize the system’s emotional and cognitive state at a very abstract
level; they are not emotions per se, but they have a large effect on the agent’s emotions. Their
intended interaction is depicted in Figure 4.15.
Fig. 4.15: Primary Interrelationships Between Psi Modulators
4.5.11 The Emergence of Emotion in the Psi Model
We now briefly review the specifics of how Psi models the emergence of emotion. The basic idea is
to define a small set of proto-emotional dimensions in terms of basic Urges and modulators.
Then, emotions are identified with regions in the space spanned by these dimensions.
The simplest approach uses a six-dimensional continuous space:
1. pleasure
92 4 Brief Survey of Cognitive Architectures
2. arousal
3. resolution level
4. selection threshold (i.e. degree of dominance of the leading motive)
5. level of background checks (the rate of the securing behavior)
6. level of goal-directed behavior
Figure 4.16 shows how the latter 5 of these dimensions are derived from underlying urges and
modulators. Note that these dimensions are not orthogonal; for instance resolution is mainly inversely
related to arousal. Additional dimensions are also discussed, for instance it is postulated
that to deal with social emotions one may wish to introduce two more demands corresponding
to inner and outer obedience to social norms, and then define dimensions in terms of these.
Fig. 4.16: Five Proto-Emotional Dimensions Implicit in the Psi Model
Specific emotions are then characterized in terms of these dimensions. According to [Bac09],
for instance, “Anger ... is characterized by high arousal, low resolution, strong motive dominance,
few background checks and strong goal-orientedness; sadness by low arousal, high resolution,
strong dominance, few background-checks and low goal-orientedness.”
I’m a bit skeptical of the contention that these dimensions fully characterize the relevant
emotions. Anger for instance seems to have some particular characteristics not implied by the
above list of dimensional values. The list of dimensional values associated with anger doesn’t
tell us that an angry person is more likely to punch someone than to bounce up and down,
for example. However, it does seem that the dimensional values associated with an emotion are
4.5 Globalist versus Localist Representations 93
informative about the emotion, so that positioning an emotion on the given dimensions tells
one a lot.
4.5.12 Knowledge Representation, Action Selection and Planning in
Psi
In addition to the basic motivation/emotion architecture of Psi, which has been adopted (with
some minor changes) for use in CogPrime, Psi has a number of other aspects that are somewhat
different from their CogPrime analogues.
First of all, on the micro level, Psi represents knowledge using structures called “quads.” Each
quad is a cluster of 5 neurons containing a core neuron, and four other neurons representing
before/after and part-of/has-part relationships in regard to that core neuron. Quads are naturally
assembled into spatiotemporal hierarchies, though they are not required to form part of
such a structure.
Psi stores knowledge using quads arranged in three networks, which are conceptually similar
to the networks in Albus’s 4D/RCS and Arel’s DeSTIN architectures:
• A sensory network, which stores declarative knowledge: schemas representing images, objects,
events and situations as hierarchical structures.
• A motor network, which contains procedural knowledge by way of hierarchical behavior
programs
• A motivational network handling demands
Perception in Psi, which is centered in the sensory network, follows principles similar to
DeSTIN (which are shared also by other systems), for instance the principle of perception as
prediction. Psi’s “HyPercept” mechanism performs hypothesis-based perception: it attempts to
predict what is there to be perceived and then attempts to verify these predictions using sensation
and memory. Furthermore HyPercept is intimately coupled with actions in the external
world, according to the concept of “Neisser’s perceptual cycle,” the cycle between exploration
and representation of reality. Perceptually acquired information is translated into schemas capable
of guiding behaviors, and these are enacted (sometimes affecting the world in significant
ways) and in the process used to guide further perception. Imaginary perceptions are handled
via a “mental stage” analogous to CogPrime’s internal simulation world.
Action selection in Psi works based on what are called “triplets,” each of which consists of
• a sensor schema (pre-conditions, “condition schema”; like CogPrime’s “context”)
• a subsequent motor schema (action, effector; like CogPrime’s “procedure”)
• a final sensor schema (post-conditions, expectations; like an CogPrime predicate or goal)
What distinguishes these triplets from classic production rules as used in (say) Soar and
ACT-R is that the triplets may be partial (some of the three elements may be missing) and
may be uncertain. However, there seems no fundamental difference between these triplets and
CogPrime’s concept/procedure/goal triplets, at a high level; the difference lies in the underlying
knowledge representation used for the schemata, and the probabilistic logic used to represent
the implication.
The work of figuring out what schema to execute to achieve the chosen goal in the current
context is done in Psi using a combination of processes called the “Rasmussen ladder” (named
94 4 Brief Survey of Cognitive Architectures
after Danish psychologist Jens Rasmussen). The Rasmussen ladder describes the organization
of action as a movement between the stages of skill-based behavior, rule-based behavior and
knowledge-based behavior, as follows:
• If a given task amounts to a trained routine, an automatism or skill is activated; it can
usually be executed without conscious attention and deliberative control.
• If there is no automatism available, a course of action might be derived from rules; before a
known set of strategies can be applied, the situation has to be analyzed and the strategies
have to be adapted.
• In those cases where the known strategies are not applicable, a way of combining the
available manipulations (operators) into reaching a given goal has to be explored at first.
This stage usually requires a recomposition of behaviors, that is, a planning process.
The planning algorithm used in the Psi and MicroPsi implementations is a fairly simple
hill-climbing planner. While it’s hypothesized that a more complex planner may be needed for
advanced intelligence, part of the Psi theory is the hypothesis that most real-life planning an
organism needs to do is fairly simple, once the organism has the right perceptual representations
and goals.
4.5.13 Psi versus CogPrime
On a high level, the similarities between Psi and CogPrime are quite strong:
• interlinked declarative, procedural and intentional knowledge structures, represented using
neural-symbolic methods (though, the knowledge structures have somewhat different highlevel
structures and low-level representational mechanisms in the two systems)
• perception via prediction and perception/action integration
• action selection via triplets that resemble uncertain, potentially partial production rules
• similar motivation/emotion framework, since CogPrime incorporates a variant of Psi for
this
On the nitty-gritty level there are many differences between the systems, but on the bigpicture
level the main difference lies in the way the cognitive synergy principle is pursued in
the two different approaches. Psi and MicroPsi rely on very simple learning algorithms that are
closely tied to the “quad” neurosymbolic knowledge representation, and hence interoperate in
a fairly natural way without need for subtle methods of “synergy engineering.” CogPrime uses
much more diverse and sophisticated learning algorithms which thus require more sophisticated
methods of interoperation in order to achieve cognitive synergy.
Chapter 5
A Generic Architecture of Human-Like Cognition
5.1 Introduction
When writing the first draft of this book, some years ago, we had the idea to explain CogPrime
by aligning its various structures and processes with the ones in the "standard architecture
diagram" of the human mind. After a bit of investigation, though, we gradually came to the
realization that no such thing existed. There was no standard flowchart or other sort of diagram
explaining the modern consensus on how human thought works. Many such diagrams
existed, but each one seemed to represent some particular focus or theory, rather than an overall
integrative understanding.
Since there are multiple opinions regarding nearly every aspect of human intelligence, it
would be difficult to get two cognitive scientists to fully agree on every aspect of an overall
human cognitive architecture diagram. Prior attempts to outline detailed mind architectures
have tended to follow highly specific theories of intelligence, and hence have attracted only
moderate interest from researchers not adhering to those theories. An example is Minsky’s work
presented in The Emotion Machine [Min07], which arguably does constitute an architecture
diagram for the human mind, but which is only loosely grounded in current empirical knowledge
and stands more as a representation of Minsky’s own intuitive understanding.
But nevertheless, it seemed to us that a reasonable attempt at an integrative, relatively
theory-neutral "human cognitive architecture diagram" would be better than nothing. So naturally,
we took it on ourselves to create such a diagram. This chapter is the result – it draws on
the thinking of a number of cognitive science and AGI researchers, integrating their perspectives
in a coherent, overall architecture diagram for human, and human-like, general intelligence. The
specific architecture diagram of CogPrime, given in Chapter 6 below, may then be understood
as a particular instantiation of this generic architecture diagram of human-like cognition.
There is no getting around the fact that, to a certain extent, the diagram presented here
reflects our particular understanding of how the mind works. However, it was intentionally
constructed with the goal of not being just an abstracted version of the CogPrime architecture
diagram! It does not reflect our own idiosyncratic understanding of human intelligence, as much
as a combination of understandings previously presented by multiple researchers (including
ourselves), arranged according to our own taste in a manner we find conceptually coherent.
With this in mind, we call it the "Integrative Human-Like Cognitive Architecture Diagram," or
for short "the integrative diagram." We have made an effort to ensure that as many pieces of
the integrative diagram as possible are well grounded in psychological and even neuroscientific
95
96 5 A Generic Architecture of Human-Like Cognition
data, rather than mainly embodying speculative notions; however, given the current state of
knowledge, this could not be done to a complete extent, and there is still some speculation
involved here and there.
While based on understandings of human intelligence, the integrative diagram is intended to
serve as an architectural outline for human-like general intelligence more broadly. For example,
CogPrime is explicitly not intended as a precise emulation of human intelligence, and does many
things quite differently than the human mind, yet can still fairly straightforwardly be mapped
into the integrative diagram.
The integrative diagram focuses on structure, but this should not be taken to represent a
valuation of structure over dynamics in our approach to intelligence. Following chapters treat
various dynamical phenomena in depth.
5.2 Key Ingredients of the Integrative Human-Like Cognitive
Architecture Diagram