The following list of key aspects of human-like intelligence has a better claim at truly being
generic and representing the consensus understanding of contemporary science. It was produced
by a very simple method: starting with the Wikipedia page for cognitive psychology, and then
adding a few items onto it based on scrutinizing the tables of contents of some top-ranked
cognitive psychology textbooks. There is some redundancy among list items, and perhaps also
some minor omissions (depending on how broadly one construes some of the items), but the
point is to give a broad indication of human mental functions as standardly identified in the
psychology field:
• Perception
– General perception
– Psychophysics
– Pattern recognition (the ability to correctly interpret ambiguous sensory information)
– Object and event recognition
– Time sensation (awareness and estimation of the passage of time)
• Motor Control
– Motor planning
– Motor execution
– Sensorimotor integration
• Categorization
– Category induction and acquisition
– Categorical judgement and classification
– Category representation and structure
– Similarity
• Memory
– Aging and memory
– Autobiographical memory
– Constructive memory
– Emotion and memory
– False memories
– Memory biases
– Long-term memory
– Episodic memory
– Semantic memory
– Procedural memory
– Short-term memory
– Sensory memory
– Working memory
• Knowledge representation
– Mental imagery
– Propositional encoding
– Imagery versus propositions as representational mechanisms
22 2 What Is Human-Like General Intelligence?
– Dual-coding theories
– Mental models
• Language
– Grammar and linguistics
– Phonetics and phonology
– Language acquisition
• Thinking
– Choice
– Concept formation
– Judgment and decision making
– Logic, formal and natural reasoning
– Problem solving
– Planning
– Numerical cognition
– Creativity
• Consciousness
– Attention and Filtering (the ability to focus mental effort on specific stimuli whilst
excluding other stimuli from consideration)
– Access consciousness
– Phenomenal consciousness
• Social Intelligence
– Distributed Cognition
– Empathy
If there’s nothing surprising to you in the above list, I’m not surprised! If you’ve read a
bit in the modern cognitive science literature, the list may even seem trivial. But it’s worth
reflecting that 50 years ago, no such list could have been produced with the same level of broad
acceptance. And less than 100 years ago, the Western world’s scientific understanding of the
mind was dominated by Freudian thinking; and not too long after that, by behaviorist thinking,
which argued that theorizing about what went on inside the mind made no sense, and science
should focus entirely on analyzing external behavior. The progress of cognitive science hasn’t
made as many headlines as contemporaneous progress in neuroscience or computing hardware
and software, but it’s certainly been dramatic. One of the reasons that AGI is more achievable
now than in the 1950s and 60s when the AI field began, is that now we understand the structures
and processes characterizing human thinking a lot better.
In spite of all the theoretical and empirical progress in the cognitive science field, however,
there is still no consensus among experts on how the various aspects of intelligence in the above
“human intelligence feature list” are achieved and interrelated. In these pages, however, for
the purpose of motivating CogPrime, we assume a broad integrative understanding roughly as
follows:
• Perception: There is significant evidence that human visual perception occurs using a
spatiotemporal hierarchy of pattern recognition modules, in which higher-level modules
2.2 Commonly Recognized Aspects of Human-like Intelligence 23
deal with broader spacetime regions, roughly as in the DeSTIN AGI architecture discussed
in Chapter 4. Further, there is evidence that each module carries out temporal predictive
pattern recognition as well as static pattern recognition. Audition likely utilizes a similar
hierarchy. Olfaction may use something more like a Hopfield attractor neural network, as
described in Chapter 13. The networks corresponding to different sense modalities have
multiple cross-linkages, more at the upper levels than the lower, and also link richly into
the parts of the mind dealing with other functions.
• Motor Control: This appears to be handled by a spatiotemporal hierarchy as well, in which
each level of the hierarchy corresponds to higher-level (in space and time) movements. The
hierarchy is very tightly linked in with the perceptual hierarchies, allowing sensorimotor
learning and coordination.
• Memory: There appear to be multiple distinct but tightly cross-linked memory systems,
corresponding to different sorts of knowledge such as declarative (facts and beliefs), procedural,
episodic, sensorimotor, attentional and intentional (goals).
• Knowledge Representation: There appear to be multiple base-level representational
systems; at least one corresponding to each memory system, but perhaps more than that.
Additionally there must be the capability to dynamically create new context-specific representational
systems founded on the base representational system.
• Language: While there is surely some innate biasing in the human mind toward learning
certain types of linguistic structure, it’s also notable that language shares a great deal of
structure with other aspects of intelligence like social roles [CB00] and the physical world
[Cas07]. Language appears to be learned based on biases toward learning certain types of
relational role systems; and language processing seems a complex mix of generic reasoning
and pattern recognition processes with specialized acoustic and syntactic processing
routines.
• Consciousness is pragmatically well-understood using Baars’ “global workspace” theory,
in which a small subset of the mind’s content is summoned at each time into a “working
memory” aka “workspace” aka “attentional focus” where it is heavily processed and used to
guide action selection.
• Thinking is a diverse combination of processes encompassing things like categorization,
(crisp and uncertain) reasoning, concept creation, pattern recognition, and others; these
processes must work well with all the different types of memory and must effectively integrate
knowledge in the global workspace with knowledge in long-term memory.
• Social Intelligence seems closely tied with language and also with self-modeling; we model
ourselves in large part using the same specialized biases we use to help us model others.
None of the points in the above bullet list is particularly controversial, but neither are any
of them universally agreed-upon by experts. However, in order to make any progress on AGI
design one must make some commitments to particular cognition-theoretic understandings, at
this level and ultimately at more precise levels as well. Further, general philosophical analyses
like the patternist philosophy to be reviewed in the following chapter only provide limited
guidance here. Patternism provides a filter for theories about specific cognitive functions – it
rules out assemblages of cognitive-function-specific theories that don’t fit together to yield a
mind that could act effectively as a pattern-recognizing, goal-achieving system with the right
internal emergent structures. But it’s not a precise enough filter to serve as a sole guide for
cognitive theory even at the high level.
The above list of points leads naturally into the integrative architecture diagram presented
in Chapter 5. But that generic architecture diagram is fairly involved, and before presenting
24 2 What Is Human-Like General Intelligence?
it, we will go through some more background regarding human-like intelligence (in the rest
of this chapter), philosophy of mind (in Chapter 3) and contemporary AGI architectures (in
Chapter4).
2.3 Further Characterizations of Humanlike Intelligence
We now present a few complementary approaches to characterizing the key aspects of humanlike
intelligence, drawn from different perspectives in the psychology and AI literature. These
different approaches all overlap substantially, which is good, yet each gives a slightly different
slant.
2.3.1 Competencies Characterizing Human-like Intelligence
First we give a list of key competencies characterizing human level intelligence resulting from
the the AGI Roadmap Workshop held at the University of Knoxville in October 2008 1 , which
was organized by Ben Goertzel and Itamar Arel. In this list, each broad competency area is
listed together with a number of specific competencies sub-areas within its scope:
1. Perception: vision, hearing, touch, proprioception, crossmodal
2. Actuation: physical skills, navigation, tool use
3. Memory: episodic, declarative, behavioral
4. Learning: imitation, reinforcement, interactive verbal instruction, written media, experimentation
5. Reasoning: deductive, abductive, inductive, causal, physical, associational, categorization
6. Planning: strategic, tactical, physical, social
7. Attention: visual, social, behavioral
8. Motivation: subgoal creation, affect-based motivation, control of emotions
9. Emotion: expressing emotion, understanding emotion
10. Self: self-awareness, self-control, other-awareness
11. Social: empathy, appropriate social behavior, social communication, social inference, group
play, theory of mind
12. Communication: gestural, pictorial, verbal, language acquisition, cross-modal
13. Quantitative: counting, grounded arithmetic, comparison, measurement
14. Building/Creation: concept formation, verbal invention, physical construction, social
group formation
Clearly this list is getting at the same things as the textbook headings given in Section 2.2,
but with a different emphasis due to its origin among AGI researchers rather than cognitive
1 See http://www.ece.utk.edu/~itamar/AGI_Roadmap.html; participants included: Sam Adams, IBM
Research; Ben Goertzel, Novamente LLC; Itamar Arel, University of Tennessee; Joscha Bach, Institute of Cognitive
Science, University of Osnabruck, Germany; Robert Coop, University of Tennessee; Rod Furlan, Singularity
Institute; Matthias Scheutz, Indiana University; J. Storrs Hall, Foresight Institute; Alexei Samsonovich, George
Mason University; Matt Schlesinger, Southern Illinois University; John Sowa, Vivomind Intelligence, Inc.; Stuart
C. Shapiro, University at Buffalo
2.3 Further Characterizations of Humanlike Intelligence 25
psychologists. As part of the AGI Roadmap project, specific tasks were created corresponding
to each of the sub-areas in the above list; we will describe some of these tasks in Chapter 17.
2.3.2 Gardner’s Theory of Multiple Intelligences
The diverse list of human-level “competencies” given above is reminiscent of Gardner’s [Gar99]
multiple intelligences (MI) framework – a psychological approach to intelligence assessment
based on the idea that different people have mental strengths in different high-level domains,
so that intelligence tests should contain aspects that focus on each of these domains separately.
MI does not contradict the “complex goals in complex environments” view of intelligence, but
rather may be interpreted as making specific commitments regarding which complex tasks and
which complex environments are most important for roughly human-like intelligence.
MI does not seek an extreme generality, in the sense that it explicitly focuses on domains
in which humans have strong innate capability as well as general-intelligence capability; there
could easily be non-human intelligences that would exceed humans according to both the commonsense
human notion of “general intelligence” and the generic “complex goals in complex
environments” or Hutter/Legg-style definitions, yet would not equal humans on the MI criteria.
This strong anthropocentrism of MI is not a problem from an AGI perspective so long as
one uses MI in an appropriate way, i.e. only for assessing the extent to which an AGI system
displays specifically human-like general intelligence. This restrictiveness is the price one pays
for having an easily articulable and relatively easily implementable evaluation framework.
Table ?? summarizes the types of intelligence included in Gardner’s MI theory.
Intelligence Type
Linguistic
Logical-Mathematical
Musical
Bodily-Kinesthetic
Spatial-Visual
Interpersonal
Aspects
Words and language, written and spoken; retention, interpretation
and explanation of ideas and information via language;
understands relationship between communication
and meaning
Logical thinking, detecting patterns, scientific reasoning
and deduction; analyse problems, perform mathematical
calculations, understands relationship between cause and
effect towards a tangible outcome
Musical ability, awareness, appreciation and use of sound;
recognition of tonal and rhythmic patterns, understands
relationship between sound and feeling
Body movement control, manual dexterity, physical agility
and balance; eye and body coordination
Visual and spatial perception; interpretation and creation
of images; pictorial imagination and expression; understands
relationship between images and meanings, and between
space and effect
Perception of other people’s feelings; relates to others; interpretation
of behaviour and communications; understands
relationships between people and their situations
Table 2.1: Types of Intelligence in Gardner’s Multiple Intelligence Theory
26 2 What Is Human-Like General Intelligence?
2.3.3 Newell’s Criteria for a Human Cognitive Architecture
Finally, another related perspective is given by Alan Newell’s “functional criteria for a human
cognitive architecture” [New90], which require that a humanlike AGI system should:
1. Behave as an (almost) arbitrary function of the environment
2. Operate in real time
3. Exhibit rational, i.e., effective adaptive behavior
4. Use vast amounts of knowledge about the environment
5. Behave robustly in the face of error, the unexpected, and the unknown
6. Integrate diverse knowledge
7. Use (natural) language
8. Exhibit self-awareness and a sense of self
9. Learn from its environment
10. Acquire capabilities through development
11. Arise through evolution
12. Be realizable within the brain
In our view, Newell’s criterion 1 is poorly-formulated, for while universal Turing computing
power is easy to come by, any finite AI system must inevitably be heavily adapted to some
particular class of environments for straightforward mathematical reasons [Hut05, GPI + 10].
On the other hand, his criteria 11 and 12 are not relevant to the CogPrime approach as we are
not doing biological modeling but rather AGI engineering. However, Newell’s criteria 2-10 are
essential in our view, and all will be covered in the following chapters.
2.3.4 intelligence and Creativity
Creativity is a key aspect of intelligence. While sometimes associated especially with geniuslevel
intelligence in science or the arts, actually creativity is pervasive throughout intelligence,
at all levels. When a child makes a flying toy car by pasting paper bird wings on his toy car, and
when a bird figures out how to use a curved stick to get a piece of food out of a difficult corner
– this is creativity, just as much as the invention of a new physics theory or the design of a new
fashion line. The very nature of intelligence – achieving complex goals in complex environments
– requires creativity for its achievement, because the nature of complex environments and goals
is that they are always unveiling new aspects, so that dealing with them involves inventing
things beyond what worked for previously known aspects.
CogPrime contains a number of cognitive dynamics that are especially effective at creating
new ideas, such as: concept creation (which synthesizes new concepts via combining aspects
of previous ones), probabilistic evolutionary learning (which simulates evolution by natural
selection, creating new procedures via mutation, combination and probabilistic modeling based
on previous ones), and analogical inference (an aspect of the Probabilistic Logic Networks
subsystems). But ultimately creativity is about how a system combines all the processes at its
disposal to synthesize novel solutions to the problems posed by its goals in its environment.
There are times, of course, when the same goal can be achieved in multiple ways – some
more creative than others. In CogPrime this relates to the existence of multiple top-level goals,
one of which may be novelty. A system with novelty as one of its goals, alongside other more
2.4 Preschool as a View into Human-like General Intelligence 27
specific goals, will have a tendency to solve other problems in creative ways, thus fulfilling its
novelty goal along with its other goals. This can be seen at the level of childlike behaviors, and
also at a much more advanced level. Salvador Dali wanted to depict his thoughts and feelings,
but he also wanted to do so in a striking and unusual way; this combination of aspirations
spurred him to produce his amazing art. A child who is asked to draw a house, but has a
goal of novelty, may draw a tower with a swimming pool on the roof rather than a typical
Colonial structure. A physical motivated by novelty will seek a non-obvious solution to the
equation at hand, rather than just applying tried and true methods, and perhaps discover
some new phenomenon. Novelty can be measured formally in terms of information-theoretic
surprisingness based upon a given basis of knowledge and experience [Sch06]; something that
is novel and creative to a child may be familiar to the adult world, and a solution that seems
novel and creative to a brilliant scientist today, may seem like cliche’ elementary school level
work 100 years from now.
Measuring creativity is even more difficult and subjective than measuring intelligence. Qualitatively,
however, we humans can recognize it; and we suspect that the qualitative emergence
of dramatic, multidisciplinary computational creativity will be one of the things that makes the
human population feel emotionally that advanced AGI has finally arrived.
2.4 Preschool as a View into Human-like General Intelligence
One issue that arises when pursuing the grand goal of human-level general intelligence is how
to measure partial progress. The classic Turing Test of imitating human conversation remains
too difficult to usefully motivate immediate-term AI research (see [HF95] [Fre90] for arguments
that it has been counterproductive for the AI field). The same holds true for comparable alternatives
like the Robot College Test of creating a robot that can attend a semester of university
and obtain passing grades. However, some researchers have suggested intermediary goals, that
constitute partial progress toward the grand goal and yet are qualitatively different from the
highly specialized problems to which most current AI systems are applied.
In this vein, Sam Adams and his team at IBM have outlined a so-called “Toddler Turing
Test,” in which one seeks to use AI to control a robot qualitatively displaying similar cognitive
behaviors to a young human child (say, a 3 year old) [AABL02]. In fact this sort of idea has a
long and venerable history in the AI field – Alan Turing’s original 1950 paper on AI [Tur50],
where he proposed the Turing Test, contains the suggestion that
"Instead of trying to produce a programme to simulate the adult mind,
why not rather try to produce one which simulates the child’s?"
We find this childlike cognition based approach promising for many reasons, including its integrative
nature: what a young child does involves a combination of perception, actuation, linguistic
and pictorial communication, social interaction, conceptual problem solving and creative
imagination. Specifically, inspired by these ideas, in Chapter 16 we will suggest the approach
of teaching and testing early-stage AGI systems in environments that emulate the preschools
used for teaching human children.
Human intelligence evolved in response to the demands of richly interactive environments,
and a preschool is specifically designed to be a richly interactive environment with the capability
to stimulate diverse mental growth. So, we are currently exploring the use of CogPrime to control
28 2 What Is Human-Like General Intelligence?
virtual agents in preschool-like virtual world environments, as well as commercial humanoid
robot platforms such as the Nao (see Figure 2.1) or Robokind (2.2) in physical preschool-like
robot labs.
Another advantage of focusing on childlike cognition is that child psychologists have created
a variety of instruments for measuring child intelligence. In Chapter 17, we will discuss an
approach to evaluating the general intelligence of human childlike AGI systems via combining
tests typically used to measure the intelligence of young human children, with additional tests
crafted based on cognitive science and the standard preschool curriculum.
To put it differently: While our long-term goal is the creation of genius machines with general
intelligence at the human level and beyond, we believe that every young child has a certain
genius; and by beginning with this childlike genius, we can built a platform capable of developing
into a genius machine with far more dramatic capabilities.
2.4.1 Design for an AGI Preschool
More precisely, we don’t suggest to place a CogPrime system in an environment that is an
exact imitation of a human preschool – this would be inappropriate since current robotic or
virtual bodies are very differently abled than the body of a young human child. But we aim to
place CogPrime in an environment emulating the basic diversity and educational character of
a typical human preschool. We stress this now, at this early point in the book, because we will
use running examples throughout the book drawn from the preschool context.
The key notion in modern preschool design is the “learning center,” an area designed and
outfitted with appropriate materials for teaching a specific skill. Learning centers are designed to
encourage learning by doing, which greatly facilitates learning processes based on reinforcement,
imitation and correction; and also to provide multiple techniques for teaching the same skills,
to accommodate different learning styles and prevent overfitting and overspecialization in the
learning of new skills.
Centers are also designed to cross-develop related skills. A “manipulatives center,” for example,
provides physical objects such as drawing implements, toys and puzzles, to facilitate
development of motor manipulation, visual discrimination, and (through sequencing and classification
games) basic logical reasoning. A “dramatics center” cross-trains interpersonal and
empathetic skills along with bodily-kinesthetic, linguistic, and musical skills. Other centers,
such as art, reading, writing, science and math centers are also designed to train not just one
area, but to center around a primary intelligence type while also cross-developing related areas.
For specific examples of the learning centers associated with particular contemporary preschools,
see [Nei98]. In many progressive, student-centered preschools, students are left largely to their
own devices to move from one center to another throughout the preschool room. Generally,
each center will be staffed by an instructor at some points in the day but not others, providing
a variety of learning experiences.
To imitate the general character of a human preschool, we will create several centers in our
robot lab. The precise architecture will be adapted via experience but initial centers will likely
be:
• a blocks center: a table with blocks on it
• a language center: a circle of chairs, intended for people to sit around and talk with the
robot
2.5 Integrative and Synergetic Approaches to Artificial General Intelligence 29
• a manipulatives center, with a variety of different objects of different shapes and sizes,
intended to teach visual and motor skills
• a ball play center: where balls are kept in chests and there is space for the robot to kick
the balls around
• a dramatics center where the robot can observe and enact various movements
One Running Example
As we proceed through the various component structures and dynamics of CogPrime in the
following chapters, it will be useful to have a few running examples to use to explain how the
various parts of the system are supposed to work. One example we will use fairly frequently is
drawn from the preschool context: the somewhat open-ended task of Build me something
out of blocks, that you haven’t built for me before, and then tell me what it is. This
is a relatively simple task that combines multiple aspects of cognition in a richly interconnected
way, and is the sort of thing that young children will naturally do in a preschool setting.
2.5 Integrative and Synergetic Approaches to Artificial General
Intelligence
In Chapter 1 we characterized CogPrime as an integrative approach. And we suggest that the
naturalness of integrative approaches to AGI follows directly from comparing above lists of
capabilities and criteria to the array of available AI technologies. No single known algorithm
or data structure appears easily capable of carrying out all these functions, so if one wants
to proceed now with creating a general intelligence that is even vaguely humanlike, one must
integrate various AI technologies within some sort of unifying architecture.
For this reason and others, an increasing amount of work in the AI community these days
is integrative in one sense or another. Estimation of Distribution Algorithms integrate probabilistic
reasoning with evolutionary learning [Pel05]. Markov Logic Networks [RD06] integrate
formal logic and probabilistic inference, as does the Probabilistic Logic Networks framework
[GIGH08] utilized in CogPrime and explained further in the book, and other works in the
“Progic” area such as [WW06]. Leslie Pack Kaelbling has synthesized low-level robotics methods
(particle filtering) with logical inference [ZPK07]. Dozens of further examples could be given.
The construction of practical robotic systems like the Stanley system that won the DARPA
Grand Challenge [Tea06] involve the integration of numerous components based on different
principles. These algorithmic and pragmatic innovations provide ample raw materials for the
construction of integrative cognitive architectures and are part of the reason why childlike AGI
is more approachable now than it was 50 or even 10 years ago.
Further, many of the cognitive architectures described in the current AI literature are “integrative”
in the sense of combining multiple, qualitatively different, interoperating algorithms.
Chapter 4 gives a high-level overview of existing cognitive architectures, dividing them into
symbolic, emergentist (e.g. neural network) and hybrid architectures. The hybrid architectures
generally integrate symbolic and neural components, often with multiple subcomponents within
each of these broad categories. However, we believe that even these excellent architectures are
not integrative enough, in the sense that they lack sufficiently rich and nuanced interactions
30 2 What Is Human-Like General Intelligence?
between the learning components associated with different kinds of memory, and hence are unlikely
to give rise to the emergent structures and dynamics characterizing general intelligence.
One of the central ideas underlying CogPrime is that with an integrative cognitive architecture
that combines multiple aspects of intelligence, achieved by diverse structures and algorithms,
within a common framework designed specifically to support robust synergetic interactions
between these aspects.
The simplest way to create an integrative AI architecture is to loosely couple multiple components
carrying out various functions, in such a way that the different components pass inputs
and outputs amongst each other but do not interfere with or modulate each others’ internal
functioning in real-time. However, the human brain appears to be integrative in a much tighter
sense, involving rich real-time dynamical coupling between various components with distinct
but related functions. In [Goe09a] we have hypothesized that the brain displays a property of
cognitive synergy, according to which multiple learning processes can not only dispatch
subproblems to each other, but also share contextual understanding in real-time, so
that each one can get help from the others in a contextually savvy way. By imbuing AI architectures
with cognitive synergy, we hypothesize, one can get past the bottlenecks that have
plagued AI in the past. Part of the reasoning here, as elaborated in Chapter 9 and [Goe09b], is
that real physical and social environments display a rich dynamic interconnection between their
various aspects, so that richly dynamically interconnected integrative AI architectures will be
able to achieve goals within them more effectively.
And this brings us to the patternist perspective on intelligent systems, alluded to above and
fleshed out further in Chapter 3 with its focus on the emergence of hierarchically and heterarchically
structured networks of patterns, and pattern-systems modeling self and others. Ultimately
the purpose of cognitive synergy in an AGI system is to enable the various AI algorithms and
structures composing the system to work together effectively enough to give rise to the right
system-wide emergent structures characterizing real-world general intelligence. The underlying
theory is that intelligence is not reliant on any particular structure or algorithm, but is reliant
on the emergence of appropriately structured networks of patterns, which can then be used to
guide ongoing dynamics of pattern recognition and creation. And the underlying hypothesis is
that the emergence of these structures cannot be achieved by a loosely interconnected assemblage
of components, no matter how sensible the architecture; it requires a tightly connected,
synergetic system.
It is possible to make these theoretical ideas about cognition mathematically rigorous; for
instance, Appendix ?? briefly presents a formal definition of cognitive synergy that has been
analyzed as part of an effort to prove theorems about the importance of cognitive synergy for
giving rise to emergent system properties associated with general intelligence. However, while
we have found such formal analyses valuable for clarifying our designs and understanding their
qualitative properties, we have concluded that, for the present, the best way to explore our
hypotheses about cognitive synergy and human-like general intelligence is empirically – via
building and testing systems like CogPrime.
2.5.1 Achieving Humanlike Intelligence via Cognitive Synergy
Summing up: at the broadest level, there are four primary challenges in constructing an integrative,
cognitive synergy based approach to AGI:
2.5 Integrative and Synergetic Approaches to Artificial General Intelligence 31
1. choosing an overall cognitive architecture that possesses adequate richness and flexibility
for the task of achieving childlike cognition.
2. Choosing appropriate AI algorithms and data structures to fulfill each of the functions
identified in the cognitive architecture (e.g. visual perception, audition, episodic memory,
language generation, analogy,...)
3. Ensuring that these algorithms and structures, within the chosen cognitive architecture,
are able to cooperate in such a way as to provide appropriate coordinated, synergetic
intelligent behavior (a critical aspect since childlike cognition is an integrated functional
response to the world, rather than a loosely coupled collection of capabilities.)
4. Embedding one’s system in an environment that provides sufficiently rich stimuli and
interactions to enable the system to use this cooperation to ongoingly, creatively develop
an intelligent internal world-model and self-model.
We argue that CogPrime
provides a viable way to address these challenges.
32 2 What Is Human-Like General Intelligence?
Fig. 2.1: The Nao humanoid robot
2.5 Integrative and Synergetic Approaches to Artificial General Intelligence 33
Fig. 2.2: The Nao humanoid robot

Chapter 3
A Patternist Philosophy of Mind
3.1 Introduction
In the last chapter we discussed human intelligence from a fairly down-to-earth perspective,
looking at the particular intelligent functions that human beings carry out in their everyday
lives. And we strongly feel this practical perspective is important: Without this concreteness, it’s
too easy for AGI research to get distracted by appealing (or frightening) abstractions of various
sorts. However, it’s also important to look at the nature of mind and intelligence from a more
general and conceptual perspective, to avoid falling into an approach that follows the particulars
of human capability but ignores the deeper structures and dynamics of mind that ultimately
allow human minds to be so capable. In this chapter we very briefly review some ideas from the
patternist philosophy of mind, a general conceptual framework on intelligence which has
been inspirational for many key aspects of the CogPrime design, and which has been ongoingly
developed by one of the authors (Ben Goertzel) during the last two decades (in a series of
publications beginning in 1991, most recently The Hidden Pattern [Goe06a]). Some of the ideas
described are quite broad and conceptual, and are related to CogPrime only via serving as
general inspirations; others are more concrete and technical, and are actually utilized within
the design itself.
CogPrime is an integrative design formed via the combination of a number of different
philosophical, scientific and engineering ideas. The success or failure of the design doesn’t depend
on any particular philosophical understanding of intelligence. In that sense, the more abstract
notions presented in this chapter should be considered “optional” rather than critical in a
CogPrime context. However, due to the core role patternism has played in the development of
CogPrime, understanding a few things about general patternist philosophy will be helpful for
understanding CogPrime, even for those readers who are not philosophically inclined. Those
readers who are philosophically inclined, on the other hand, are urged to read The Hidden
Pattern and then interpret the particulars of CogPrime in this light.
3.2 Some Patternist Principles
The patternist philosophy of mind is a general approach to thinking about intelligent systems.
It is based on the very simple premise that mind is made of pattern – and that a mind is a
35
36 3 A Patternist Philosophy of Mind
system for recognizing patterns in itself and the world, critically including patterns regarding
which procedures are likely to lead to the achievement of which goals in which contexts.
Pattern as the basis of mind is not in itself is a very novel idea; this concept is present, for
instance, in the 19th-century philosophy of Charles Peirce [Pei34], in the writings of contemporary
philosophers Daniel Dennett [Den91] and Douglas Hofstadter [Hof79, Hof96], in Benjamin
Whorf’s [Who64] linguistic philosophy and Gregory Bateson’s [Bat79] systems theory of mind
and nature. Bateson spoke of the Metapattern: “that it is pattern which connects.” In Goertzel’s
writings on philosophy of mind, an effort has been made to pursue this theme more thoroughly
than has been done before, and to articulate in detail how various aspects of human mind and
mind in general can be well-understood by explicitly adopting a patternist perspective. 1
In the patternist perspective, "pattern" is generally defined as "representation as something
simpler." Thus, for example, if one measures simplicity in terms of bit-count, then a program
compressing an image would be a pattern in that image. But if one uses a simplicity measure
incorporating run-time as well as bit-count, then the compressed version may or may not be a
pattern in the image, depending on how one’s simplicity measure weights the two factors. This
definition encompasses simple repeated patterns, but also much more complex ones. While
pattern theory has typically been elaborated in the context of computational theory, it is not
intrinsically tied to computation; rather, it can be developed in any context where there is a
notion of "representation" or "production" and a way of measuring simplicity. One just needs
to be able to assess the extent to which f represents or produces X, and then to compare the
simplicity of f and X; and then one can assess whether f is a pattern in X. A formalization of
this notion of pattern is given in [Goe06a] and briefly summarized at the end of this chapter.
Next, in patternism the mind of an intelligent system is conceived as the (fuzzy) set of
patterns in that system, and the set of patterns emergent between that system and other
systems with which it interacts. The latter clause means that the patternist perspective is
inclusive of notions of distributed intelligence [Hut96]. Basically, the mind of a system is the
fuzzy set of different simplifying representations of that system that may be adopted.
Intelligence is conceived, similarly to in Marcus Hutter’s [Hut05] recent work (and as elaborated
informally in Chapter 2 above, and formally in Chapter 7 below), as the ability to achieve
complex goals in complex environments; where complexity itself may be defined as the possession
of a rich variety of patterns. A mind is thus a collection of patterns that is associated
with a persistent dynamical process that achieves highly-patterned goals in highly-patterned
environments.
An additional hypothesis made within the patternist philosophy of mind is that reflection is
critical to intelligence. This lets us conceive an intelligent system as a dynamical system that
recognizes patterns in its environment and itself, as part of its quest to achieve complex goals.
While this approach is quite general, it is not vacuous; it gives a particular structure to the
tasks of analyzing and synthesizing intelligent systems. About any would-be intelligent system,
we are led to ask questions such as:
• How are patterns represented in the system? That is, how does the underlying infrastructure
of the system give rise to the displaying of a particular pattern in the system’s behavior?
• What kinds of patterns are most compactly represented within the system?
• What kinds of patterns are most simply learned?
1 In some prior writings the term “psynet model of mind” has been used to refer to the application of patternist
philosophy to cognitive theory, but this term has been "deprecated" in recent publications as it seemed to
introduce more confusion than clarification.
3.2 Some Patternist Principles 37
• What learning processes are utilized for recognizing patterns?
• What mechanisms are used to give the system the ability to introspect (so that it can
recognize patterns in itself)?
Now, these same sorts of questions could be asked if one substituted the word “pattern” with
other words like “knowledge” or “information”. However, we have found that asking these questions
in the context of pattern leads to more productive answers, avoiding unproductive byways
and also tying in very nicely with the details of various existing formalisms and algorithms for
knowledge representation and learning.
Among the many kinds of patterns in intelligent systems, semiotic patterns are particularly
interesting ones. Peirce decomposed these into three categories:
• iconic patterns, which are patterns of contextually important internal similarity between
two entities (e.g. an iconic pattern binds a picture of a person to that person)
• indexical patterns, which are patterns of spatiotemporal co-occurrence (e.g. an indexical
pattern binds a wedding dress and a wedding)
• symbolic patterns, which are patterns indicating that two entities are often involved in
the same relationships (e.g. a symbolic pattern between the number “5” (the symbol) and
various sets of 5 objects (the entities that the symbol is taken to represent))
Of course, some patterns may span more than one of these semiotic categories; and there
are also some patterns that don’t fall neatly into any of these categories. But the semiotic
patterns are particularly important ones; and symbolic patterns have played an especially large
role in the history of AI, because of the radically different approaches different researchers have
taken to handling them in their AI systems. Mathematical logic and related formalisms provide
sophisticated mechanisms for combining and relating symbolic patterns (“symbols”), and some
AI approaches have focused heavily on these, sometimes more so than on the identification of
symbolic patterns in experience or the use of them to achieve practical goals. We will look fairly
carefully at these differences in Chapter 4.
Pursuing the patternist philosophy in detail leads to a variety of particular hypotheses and
conclusions about the nature of mind. Following from the view of intelligence in terms of
achieving complex goals in complex environments, comes a view in which the dynamics of
a cognitive system are understood to be governed by two main forces:
• self-organization, via which system dynamics cause existing system patterns to give rise to
new ones
• goal-oriented behavior, which will be defined more rigorously in Chapter 7, but basically
amounts to a system interacting with its environment in a way that appears like an attempt
to maximize some reasonably simple function
Self-organized and goal-oriented behavior must be understood as cooperative aspects. If an
agent is asked to build a surprising structure out of blocks and does so, this is goal-oriented.
But the agent’s ability to carry out this goal-oriented task will be greater if it has previously
played around with blocks a lot in an unstructured, spontaneous way. And the “nudge toward
creativity” given to it by asking it to build a surprising blocks structure may cause it to explore
some novel patterns, which then feed into its future unstructured blocks play.
Based on these concepts, as argued in detail in [Goe06a], several primary dynamical principles
may be posited, including:
38 3 A Patternist Philosophy of Mind
• Evolution , conceived as a general process via which patterns within a large population
thereof are differentially selected and used as the basis for formation of new patterns, based
on some “fitness function” that is generally tied to the goals of the agent
– Example: If trying to build a blocks structure that will surprise Bob, an agent may
simulate several procedures for building blocks structures in its “mind’s eye”, assessing
for each one the expected degree to which it might surprise Bob. The search through
procedure space could be conducted as a form of evolution, via an algorithm such as
MOSES.
• Autopoiesis: the process by which a system of interrelated patterns maintains its integrity,
via a dynamic in which whenever one of the patterns in the system begins to decrease in
intensity, some of the other patterns increase their intensity in a manner that causes the
troubled pattern to increase in intensity again
– Example: An agent’s set of strategies for building the base of a tower, and its set of
strategies for building the middle part of a tower, are likely to relate autopoietically. If
the system partially forgets how to build the base of a tower, then it may regenerate
this missing knowledge via using its knowledge about how to build the middle part
(i.e., it knows it needs to build the base in a way that will support good middle parts).
Similarly if it partially forgets how to build the middle part, then it may regenerate this
missing knowledge via using its knowledge about how to build the base (i.e. it knows a
good middle part should fit in well with the sorts of base it knows are good).
– This same sort of interdependence occurs between pattern-sets containing more than
two elements
– Sometimes (as in the above example) autopoietic interdependence in the mind is tied
to interdependencies in the physical world, sometimes not.
• Association. Patterns, when given attention, spread some of this attention to other patterns
that they have previously been associated with in some way. Furthermore, there is
Peirce’s law of mind [Pei34], which could be paraphrased in modern terms as stating that
the mind is an associative memory network, whose dynamics dictate that every idea in
the memory is an active agent, continually acting on those ideas with which the memory
associates it.
– Example: Building a blocks structure that resembles a tower, spreads attention to memories
of prior towers the agents has seen, and also to memories of people the agent knows
have seen towers, and structures it has built at the same time as towers, structures that
resemble towers in various respects, etc.
• Differential attention allocation / credit assignment. Patterns that have been valuable
for goal-achievement are given more attention, and are encouraged to participate in
giving rise to new patterns.
– Example: Perhaps in a prior instance of the task “build me a surprising structure out of
blocks,” searching through memory for non-blocks structures that the agent has played
with has proved a useful cognitive strategy. In that case, when the task is posed to the
agent again, it should tend to allocate disproportionate resources to this strategy.
• Pattern creation. Patterns that have been valuable for goal-achievement are mutated and
combined with each other to yield new patterns.
3.2 Some Patternist Principles 39
– Example: Building towers has been useful in a certain context, but so has building
structures with a large number of triangles. Why not build a tower out of triangles?
Or maybe a vaguely tower-like structure that uses more triangles than a tower easily
could?
– Example: Building an elongated block structure resembling a table was successful in the
past, as was building a structure resembling a very flat version of a chair. Generalizing,
maybe building distorted versions of furniture is good. Or maybe it is building distorted
version of any previously perceived objects that is good. Or maybe both, to different
degrees....
Next, for a variety of reasons outlined in [Goe06a] it becomes appealing to hypothesize that the
network of patterns in an intelligent system must give rise to the following large-scale emergent
structures
• Hierarchical network. Patterns are habitually in relations of control over other patterns that
represent more specialized aspects of themselves.
– Example: The pattern associated with “tall building” has some control over the pattern
associated with “tower”, as the former represents a more general concept ... and “tower”
has some control over “Eiffel tower”, etc.
• Heterarchical network. The system retains a memory of which patterns have previously
been associated with each other in any way.
– Example: “Tower” and “snake” are distant in the natural pattern hierarchy, but may be
associatively/heterarchically linked due to having a common elongated structure. This
heterarchical linkage may be used for many things, e.g. it might inspire the creative
construction of a tower with a snake’s head.
• Dual network. Hierarchical and heterarchical structures are combined, with the dynamics
of the two structures working together harmoniously. Among many possible ways to hierarchically
organize a set of patterns, the one used should be one that causes hierarchically
nearby patterns to have many meaningful heterarchical connections; and of course, there
should be a tendency to search for heterarchical connections among hierarchically nearby
patterns.
– Example: While the set of patterns hierarchically nearby “tower” and the set of patterns
heterarchically nearby “tower” will be quite different, they should still have more overlap
than random pattern-sets of similar sizes. So, if looking for something else heterarchically
near “tower”, using the hierarchical information about “tower” should be of some use,
and vice versa.
– In PLN, hierarchical relationships correspond to Atoms A and B so that InheritanceAB
and InheritanceBA have highly dissimilar strength; and heterarchical relationships correspond
to IntensionalSimilarity relationships. The dual network structure then arises
when intensional and extensional inheritance approximately correlate with each other,
so that inference about either kind of inheritance assists with figuring out about the
other kind.
• Self structure. A portion of the network of patterns forms into an approximate image of the
overall network of patterns.
40 3 A Patternist Philosophy of Mind
– Example: Each time the agent builds a certain structure, it observes itself building
the structure, and its role as “builder of a tall tower” (or whatever the structure is)
becomes part of its self-model. Then when it is asked to build something new, it may
consult its self-model to see if it believes itself capable of building that sort of thing (for
instance, if it is asked to build something very large, its self-model may tell it that it
lacks persistence for such projects, so it may reply “I can try, but I may wind up not
finishing it”).
As we proceed through the CogPrime design in the following pages, we will see how each
of these abstract concepts arises concretely from CogPrime’s structures and algorithms. If the
theory of [Goe06a] is correct, then the success of CogPrime as a design will depend largely on
whether these high-level structures and dynamics can be made to emerge from the synergetic
interaction of CogPrime’s representation and algorithms, when they are utilized to control an
appropriate agent in an appropriate environment.
3.3 Cognitive Synergy
Now we dig a little deeper and present a different sort of “general principle of feasible general
intelligence”, already hinted in earlier chapters: the cognitive synergy principle 2 , which is both
a conceptual hypothesis about the structure of generally intelligent systems in certain classes of
environments, and a design principle used to guide the design of CogPrime. Chapter 8 presents
a mathematical formalization of the notion of cognitive synergy; here we present the conceptual
idea informally, which makes it more easily digestible but also more vague-sounding.
We will focus here on cognitive synergy specifically in the case of “multi-memory systems,”
which we define as intelligent systems whose combination of environment, embodiment and
motivational system make it important for them to possess memories that divide into partially
but not wholly distinct components corresponding to the categories of:
• Declarative memory
– Examples of declarative knowledge: Towers on average are taller than buildings. I generally
am better at building structures I imagine, than at imitating structures I’m shown
in pictures.
• Procedural memory (memory about how to do certain things)
– Examples of procedural knowledge: Practical know-how regarding how to pick up an
elongated rectangular block, or a square one. Know-how regarding when to approach
a problem by asking “What would one of my teachers do in this situation” versus by
thinking through the problem from first principles.
• Sensory and episodic memory
– Example of sensory knowledge: memory of Bob’s face; memory of what a specific tall
blocks tower looked like
2 While these points are implicit in the theory of mind given in [Goe06a], they are not articulated in this
specific form there. So the material presented in this section is a new development within patternist philosophy,
developed since [Goe06a] in a series of conference papers such as [Goe09a].
3.3 Cognitive Synergy 41
– Example of episodic knowledge: memory of the situation in which the agent first met
Bob; memory of a situation in which a specific tall blocks tower was built
• Attentional memory (knowledge about what to pay attention to in what contexts)
– Example of attentional knowledge: When involved with a new person, it’s useful to pay
attention to whatever that person looks at
• Intentional memory (knowledge about the system’s own goals and subgoals)
– Example of intentional knowledge: If my goal is to please some person whom I don’t
know that well, then a subgoal may be figuring out what makes that person smile.
In Chapter 9 below we present a detailed argument as to how the requirement for a multimemory
underpinning for general intelligence emerges from certain underlying assumptions
regarding the measurement of the simplicity of goals and environments. Specifically we argue
that each of these memory types corresponds to certain modes of communication, so that intelligent
agents which have to efficiently handle a sufficient variety of types of communication with
other agents, are going to have to handle all these types of memory. These types of communication
overlap and are often used together, which implies that the different memories and their
associated cognitive processes need to work together. The points made in this section do not
rely on that argument regarding the relation of multiple memory types to the environmental
situation of multiple communication types. What they do rely on is the assumption that, in
the intelligence agent in question, the different components of memory are significantly but not
wholly distinct. That is, there are significant “family resemblances” between the memories of a
single type, yet there are also thoroughgoing connections between memories of different types.
Repeating the above points in a slightly more organized manner and then extending them, the
essential idea of cognitive synergy, in the context of multi-memory systems, may be expressed
in terms of the following points
1. Intelligence, relative to a certain set of environments, may be understood as the capability
to achieve complex goals in these environments.
2. With respect to certain classes of goals and environments, an intelligent system requires a
“multi-memory” architecture, meaning the possession of a number of specialized yet interconnected
knowledge types, including: declarative, procedural, attentional, sensory, episodic
and intentional (goal-related). These knowledge types may be viewed as different sorts of
patterns that a system recognizes in itself and its environment.
3. Such a system must possess knowledge creation (i.e. pattern recognition / formation) mechanisms
corresponding to each of these memory types. These mechanisms are also called
“cognitive processes.”
4. Each of these cognitive processes, to be effective, must have the capability to recognize when
it lacks the information to perform effectively on its own; and in this case, to dynamically
and interactively draw information from knowledge creation mechanisms dealing with other
types of knowledge
5. This cross-mechanism interaction must have the result of enabling the knowledge creation
mechanisms to perform much more effectively in combination than they would if operated
non-interactively. This is “cognitive synergy.”
Interactions as mentioned in Points 4 and 5 in the above list are the real conceptual meat
of the cognitive synergy idea. One way to express the key idea here, in an AI context, is that
42 3 A Patternist Philosophy of Mind
most AI algorithms suffer from combinatorial explosions: the number of possible elements to
be combined in a synthesis or analysis is just too great, and the algorithms are unable to
filter through all the possibilities, given the lack of intrinsic constraint that comes along with
a “general intelligence” context (as opposed to a narrow-AI problem like chess-playing, where
the context is constrained and hence restricts the scope of possible combinations that needs
to be considered). In an AGI architecture based on cognitive synergy, the different learning
mechanisms must be designed specifically to interact in such a way as to palliate each others’
combinatorial explosions - so that, for instance, each learning mechanism dealing with a certain
sort of knowledge, must synergize with learning mechanisms dealing with the other sorts of
knowledge, in a way that decreases the severity of combinatorial explosion.
One prerequisite for cognitive synergy to work is that each learning mechanism must recognize
when it is “stuck,” meaning it’s in a situation where it has inadequate information to
make a confident judgment about what steps to take next. Then, when it does recognize that
it’s stuck, it may request help from other, complementary cognitive mechanisms.
3.4 The General Structure of Cognitive Dynamics: Analysis and
Synthesis
We have discussed the need for synergetic interrelation between cognitive processes corresponding
to different types of memory ... and the general high-level cognitive dynamics that a mind
must possess (evolution, autopoiesis). The next step is to dig further into the nature of the cognitive
processes associated with different memory types and how they give rise to the needed
high-level cognitive dynamics. In this section we present a general theory of cognitive processes
based on a decomposition of cognitive processes into the two categories of analysis and synthesis,
and a general formulation of each of these categories 3 .
Specifically we focus here on what we call focused cognitive processes; that is, cognitive
processes that selectively focus attention on a subset of the patterns making up a mind. In
general these are not the only kind, there may also be global cognitive processes that act on
every pattern in a mind. An example of a global cognitive process in CogPrime is the basic
attention allocation process, which spreads “importance” among all knowledge in the system’s
memory. Global cognitive processes are also important, but focused cognitive processes are
subtler to understand which is why we spend more time on them here.
3.4.1 Component-Systems and Self-Generating Systems
We begin with autopoesis – and, more specifically, with the concept of a “component-system”,
as described in George Kampis’s book Self-Modifying Systems in Biology and Cognitive Science
[Kam91], and as modified into the concept of a “self-generating system” or SGS in Goertzel’s
book Chaotic Logic [Goe94]. Roughly speaking, a Kampis-style component-system consists of
a set of components that combine with each other to form other compound components. The
3 While these points are highly compatible with theory of mind given in [Goe06a], they are not articulated there.
The material presented in this section is a new development within patternist philosophy, presented previously
only in the article [GPPG06].
3.4 The General Structure of Cognitive Dynamics: Analysis and Synthesis 43
metaphor Kampis uses is that of Lego blocks, combining to form bigger Lego structures. Compound
structures may in turn be combined together to form yet bigger compound structures.
A self-generating system is basically the same concept as a component-system, but understood
to be computable, whereas Kampis claims that component-systems are uncomputable.
Next, in SGS theory there is also a notion of reduction (not present in the Lego metaphor):
sometimes when components are combined in a certain way, a “reaction” happens, which may
lead to the elimination of some of the components. One relevant metaphor here is chemistry.
Another is abstract algebra: for instance, if we combine a component f with its “inverse” component
f −1 , both components are eliminated. Thus, we may think about two stages in the
interaction of sets of components: combination, and reduction. Reduction may be thought of
as algebraic simplification, governed by a set of rules that apply to a newly created compound
component, based on the components that are assembled within it.
Formally, suppose C 1 , C 2 , ... is the set of components present in a discrete-time componentsystem
at time t. Then, the components present at time t+1 are a subset of the set of components
of the form
Reduce(Join(C i (1), ..., C i (r)))
where Join is a joining operation, and Reduce is a reduction operator. The joining operation
is assumed to map tuples of components into components, and the reduction operator is assumed
to map the space of components into itself. Of course, the specific nature of a component system
is totally dependent on the particular definitions of the reduction and joining operators; in
following chapters we will specify these for the CogPrime system, but for the purpose of the
broader theoretical discussion in this section they may be left general.
What is called the “cognitive equation” in Chaotic Logic [Goe94] is the case of a SGS where
the patterns in the system at time t have a tendency to correspond to components of the system
at future times t + s. So, part of the action of the system is to transform implicit knowledge
(patterns among system components) into explicit knowledge (specific system components). We
will see one version of this phenomenon in Chapter 14 where we model implicit knowledge using
mathematical structures called “derived hypergraphs”; and we will also later review several ways
in which CogPrime’s dynamics explicitly encourage cognitive-equation type dynamics, e.g.:
• inference, which takes conclusions implicit in the combination of logical relationships, and
makes them implicit by deriving new logical relationships from them
• map formation, which takes concepts that have often been active together, and creates new
concepts grouping them
• association learning, which creates links representing patterns of association between entities
• probabilistic procedure learning, which creates new models embodying patterns regarding
which procedures tend to perform well according to particular fitness functions
3.4.2 Analysis and Synthesis
Now we move on to the main point of this section: the argument that all or nearly all focused
cognitive processes are expressible using two general process-schemata we call synthesis and
44 3 A Patternist Philosophy of Mind
analysis 4 . The notion of “focused cognitive process” will be exemplified more thoroughly below,
but in essence what is meant is a cognitive process that begins with a small number of items
(drawn from memory) as its focus, and has as its goal discovering something about these
items, or discovering something about something else in the context of these items or in a way
strongly biased by these items. This is different from a global cognitive process whose goal is
more broadly-based and explicitly involves all or a large percentage of the knowledge in an
intelligent system’s memory store.
Among the focused cognitive processes are those governed by the so-called cognitive schematic
implication
Context ∧ P rocedure → Goal
where the Context involves sensory, episodic and/or declarative knowledge; and attentional
knowledge is used to regulate how much resource is given to each such schematic implication in
memory. Synergy among the learning processes dealing with the context, the procedure and the
goal is critical to the adequate execution of the cognitive schematic using feasible computational
resources. This sort of explicitly goal-driven cognition plays a significant though not necessarily
dominant role in CogPrime, and is also related to production rules systems and other traditional
AI systems, as will be articulated in Chapter 4.
The synthesis and analysis processes as we conceive them, in the general framework of SGS
theory, are as follows. First, synthesis, as shown in Figure 3.1, is defined as
synthesis: Iteratively build compounds from the initial component pool using the combinators,
greedily seeking compounds that seem likely to achieve the goal.
Or in more detail:
1. Begin with some initial components (the initial “current pool”), an additional set of components
identified as “combinators” (combination operators), and a goal function
2. Combine the components in the current pool, utilizing the combinators, to form product
components in various ways, carrying out reductions as appropriate, and calculating relevant
quantities associated with components as needed
3. Select the product components that seem most promising according to the goal function,
and add these to the current pool (or else simply define these as the current pool)
4. Return to Step 2
And analysis, as shown in Figure 3.2, is defined as
analysis: Iteratively search (the system’s long-term memory) for component-sets that combine
using the combinators to form the initial component pool (or subsets thereof), greedily
seeking component-sets that seem likely to achieve the goal
or in more detail:
1. Begin with some components (the initial “current pool”) and a goal function
2. Seek components so that, if one combines them to form product components using the
combinators and then performs appropriate reductions, one obtains (as many as possible
of) the components in the current pool
4 In [GPPG06], what is here called “analysis” was called “backward synthesis”, a name which has some advantages
since it indicated that what’s happening is a form of creation; but here we have opted for the more traditional
analysis/synthesis terminology
3.4 The General Structure of Cognitive Dynamics: Analysis and Synthesis 45
Fig. 3.1: The General Process of Synthesis
3. Use the newly found constructions of the components in the current pool, to update the
quantitative properties of the components in the current pool, and also (via the current
pool) the quantitative properties of the components in the initial pool
4. Out of the components found in Step 2, select the ones that seem most promising according
to the goal function, and add these to the current pool (or else simply define these as the
current pool)
5. Return to Step 2
More formally, synthesis may be specified as follows. Let X denote the set of combinators,
and let Y 0 denote the initial pool of components (the initial focus of the cognitive process).
Given Y i , let Z i denote the set
Reduce(Join(C i (1), ..., C i (r)))
where the C i are drawn from Y i or from X. We may then say
Y i+1 = F ilter(Z i )
where F ilter is a function that selects a subset of its arguments.
Analysis, on the other hand, begins with a set W of components, and a set X of combinators,
and tries to find a series Y i so that according to the process of synthesis, Y n =W .
In practice, of course, the implementation of a synthesis process need not involve the explicit
construction of the full set Z i . Rather, the filtering operation takes place implicitly during the
construction of Y i+1 . The result, however, is that one gets some subset of the compounds producible
via joining and reduction from the set of components present in Y i plus the combinators
X.
46 3 A Patternist Philosophy of Mind
Fig. 3.2: The General Process of Analysis
Conceptually one may view synthesis as a very generic sort of “growth process,” and analysis
as a very generic sort of “figuring out how to grow something.” The intuitive idea underlying
the present proposal is that these forward-going and backward-going “growth processes” are
among the essential foundations of cognitive control, and that a conceptually sound design for
cognitive control should explicitly make use of this fact. To abstract away from the details,
what these processes are about is:
• taking the general dynamic of compound-formation and reduction as outlined in Kampis
and Chaotic Logic
• introducing goal-directed pruning (“filtering”) into this dynamic so as to account for the
limitations of computational resources that are a necessary part of pragmatic intelligence
3.4.3 The Dynamic of Iterative Analysis and Synthesis
While synthesis and analysis are both very useful on their own, they achieve their greatest power
when harnessed together. It is my hypothesis that the dynamic pattern of alternating synthesis
and analysis has a fundamental role in cognition. Put simply, synthesis creates new mental
forms by combining existing ones. Then, analysis seeks simple explanations for the forms in the
mind, including the newly created ones; and, this explanation itself then comprises additional
new forms in the mind, to be used as fodder for the next round of synthesis. Or, to put it yet
more simply:
3.4 The General Structure of Cognitive Dynamics: Analysis and Synthesis 47
⇒ Combine ⇒ Explain ⇒ Combine ⇒ Explain ⇒ Combine ⇒
It is not hard to express this alternating dynamic more formally, as well.
• Let X denote any set of components.
• Let F(X) denote a set of components which is the result of synthesis on X.
• Let B(X) denote a set of components which is the result of analysis of X. We assume also
a heuristic biasing the synthesis process toward simple constructs.
• Let S(t) denote a set of components at time t, representing part of a system’s knowledge
base.
• Let I(t) denote components resulting from the external environment at time t.
Then, we may consider a dynamical iteration of the form
S(t + 1) = B(F (S(t) + I(t)))
This expresses the notion of alternating synthesis and analysis formally, as a dynamical
iteration on the space of sets of components. We may then speak about attractors of this
iteration: fixed points, limit cycles and strange attractors. One of the key hypotheses I wish
to put forward here is that some key emergent cognitive structures are strange attractors of
this equation. The iterative dynamic of combination and explanation leads to the emergence
of certain complex structures that are, in essence, maintained when one recombines their parts
and then seeks to explain the recombinations. These structures are built in the first place
through iterative recombination and explanation, and then survive in the mind because they
are conserved by this process. They then ongoingly guide the construction and destruction of
various other temporary mental structures that are not so conserved.
3.4.4 Self and Focused Attention as Approximate Attractors of the
Dynamic of Iterated Forward-Analysis
As noted above, patternist philosophy argues that two key aspects of intelligence are emergent
structures that may be called the “self” and the “attentional focus.” These, it is suggested, are
aspects of intelligence that may not effectively be wired into the infrastructure of an intelligent
system, though of course the infrastructure may be configured in such a way as to encourage
their emergence. Rather, these aspects, by their nature, are only likely to be effective if they
emerge from the cooperative activity of various cognitive processes acting within a broad base
of knowledge.
Above we have described the pattern of ongoing habitual oscillation between synthesis and
analysis as a kind of “dynamical iteration.” Here we will argue that both self and attentional
focus may be viewed as strange attractors of this iteration. The mode of argument is relatively
informal. The essential processes under consideration are ones that are poorly understood from
an empirical perspective, due to the extreme difficulty involved in studying them experimentally.
For understanding self and attentional focus, we are stuck in large part with introspection, which
is famously unreliable in some contexts, yet still dramatically better than having no information
at all. So, the philosophical perspective on self and attentional focus given here is a synthesis of
empirical and introspective notions, drawn largely from the published thinking and research of
48 3 A Patternist Philosophy of Mind
others but with a few original twists. From a CogPrime perspective, its use has been to guide
the design process, to provide a grounding for what otherwise would have been fairly arbitrary
choices.
3.4.4.1 Self
Another high-level intelligent system pattern mentioned above is the “self”, which we here will tie
in with analysis and synthesis processes. The term “self” as used here refers to the “phenomenal
self” [Met04] or “self-model”. That is, the self is the model that a system builds internally,
reflecting the patterns observed in the (external and internal) world that directly pertain to
the system itself. As is well known in everyday human life, self-models need not be completely
accurate to be useful; and in the presence of certain psychological factors, a more accurate
self-model may not necessarily be advantageous. But a self-model that is too badly inaccurate
will lead to a badly-functioning system that is unable to effectively act toward the achievement
of its own goals.
The value of a self-model for any intelligent system carrying out embodied agentive cognition
is obvious. And beyond this, another primary use of the self is as a foundation for metaphors
and analogies in various domains. Patterns recognized pertaining to the self are analogically
extended to other entities. In some cases this leads to conceptual pathologies, such as the anthropomorphization
of trees, rocks and other such objects that one sees in some precivilized
cultures. But in other cases this kind of analogy leads to robust sorts of reasoning - for instance,
in reading Lakoff and Nunez’s [LN00] intriguing explorations of the cognitive foundations of
mathematics, it is pretty easy to see that most of the metaphors on which they hypothesize
mathematics to be based, are grounded in the mind’s conceptualization of itself as a spatiotemporally
embedded entity, which in turn is predicated on the mind’s having a conceptualization
of itself (a self) in the first place.
A self-model can in many cases form a self-fulfilling prophecy (to make an obvious doubleentendre’!).
Actions are generated based on one’s model of what sorts of actions one can and/or
should take; and the results of these actions are then incorporated into one’s self-model. If a
self-model proves a generally bad guide to action selection, this may never be discovered, unless
said self-model includes the knowledge that semi-random experimentation is often useful.
In what sense, then, may it be said that self is an attractor of iterated analysis? Analysis
infers the self from observations of system behavior. The system asks: What kind of system
might I be, in order to give rise to these behaviors that I observe myself carrying out? Based
on asking itself this question, it constructs a model of itself, i.e. it constructs a self. Then, this
self guides the system’s behavior: it builds new logical relationships its self-model and various
other entities, in order to guide its future actions oriented toward achieving its goals. Based on
the behaviors newly induced via this constructive, forward-synthesis activity, the system may
then engage in analysis again and ask: What must I be now, in order to have carried out these
new actions? And so on.
Our hypothesis is that after repeated iterations of this sort, in infancy, finally during early
childhood a kind of self-reinforcing attractor occurs, and we have a self-model that is resilient
and doesn’t change dramatically when new instances of action- or explanation-generation occur.
This is not strictly a mathematical attractor, though, because over a long period of time the self
may well shift significantly. But, for a mature self, many hundreds of thousands or millions of
forward-analysis cycles may occur before the self-model is dramatically modified. For relatively
3.4 The General Structure of Cognitive Dynamics: Analysis and Synthesis 49
long periods of time, small changes within the context of the existing self may suffice to allow
the system to control itself intelligently.
Humans can also develop what are known as subselves [Row90]. A subself is a partially
autonomous self-network focused on particular tasks, environments or interactions. It contains
a unique model of the whole organism, and generally has its own set of episodic memories,
consisting of memories of those intervals during which it was the primary dynamic mode controlling
the organism. One common example is the creative subself – the subpersonality that
takes over when a creative person launches into the process of creating something. In these
times, a whole different personality sometimes emerges, with a different sort of relationship
to the world. Among other factors, creativity requires a certain open-ness that is not always
productive in an everyday life context, so it’s natural for the self-system of a highly creative
person to bifurcate into one self-system for everyday life, and another for the protected context
of creative activity. This sort of phenomenon might emerge naturally in CogPrime systems as
well if they were exposed to appropriate environments and social situations.
Finally, it is interesting to speculate regarding how self may differ in future AI systems as
opposed to in humans. The relative stability we see in human selves may not exist in AI systems
that can self-improve and change more fundamentally and rapidly than humans can. There may
be a situation in which, as soon as a system has understood itself decently, it radically modifies
itself and hence violates its existing self-model. Thus: intelligence without a long-term stable self.
In this case the “attractor-ish” nature of the self holds only over much shorter time scales than
for human minds or human-like minds. But the alternating process of synthesis and analysis
for self-construction is still critical, even though no reasonably stable self-constituting attractor
ever emerges. The psychology of such intelligent systems will almost surely be beyond human
beings’ capacity for comprehension and empathy.
3.4.4.2 Attentional Focus
Finally, we turn to the notion of an “attentional focus” similar to Baars’ [Baa97] notion of a
Global Workspace, which will be reviewed in more detail in Chapter 4: a collection of mental
entities that are, at a given moment, receiving far more than the usual share of an intelligent
system’s computational resources. Due to the amount of attention paid to items in the attentional
focus, at any given moment these items are in large part driving the cognitive processes
going on elsewhere in the mind as well - because the cognitive processes acting on the items in
the attentional focus are often involved in other mental items, not in attentional focus, as well
(and sometimes this results in pulling these other items into attentional focus). An intelligent
system must constantly shift its attentional focus from one set of entities to another based on
changes in its environment and based on its own shifting discoveries.
In the human mind, there is a self-reinforcing dynamic pertaining to the collection of entities
in the attentional focus at any given point in time, resulting from the observation that: If A
is in the attentional focus, and A and B have often been associated in the past, then odds
are increased that B will soon be in the attentional focus. This basic observation has been
refined tremendously via a large body of cognitive psychology work; and neurologically it follows
not only from Hebb’s [Heb49] classic work on neural reinforcement learning, but also from
numerous more modern refinements [SB98]. But it implies that two items A and B, if both in
the attentional focus, can reinforce each others’ presence in the attentional focus, hence forming
a kind of conspiracy to keep each other in the limelight. But of course, this kind of dynamic
50 3 A Patternist Philosophy of Mind
must be counteracted by a pragmatic tendency to remove items from the attentional focus if
giving them attention is not providing sufficient utility in terms of the achievement of system
goals.
The synthesis and analysis perspective provides a more systematic perspective on this selfreinforcing
dynamic. Synthesis occurs in the attentional focus when two or more items in the
focus are combined to form new items, new relationships, new ideas. This happens continually,
as one of the main purposes of the attentional focus is combinational. On the other hand,
Analysis then occurs when a combination that has been speculatively formed is then linked
in with the remainder of the mind (the “unconscious”, the vast body of knowledge that is not
in the attentional focus at the given moment in time). Analysis basically checks to see what
support the new combination has within the existing knowledge store of the system. Thus,
forward-analysis basically comes down to “generate and test”, where the testing takes the form
of attempting to integrate the generated structures with the ideas in the unconscious longterm
memory. One of the most obvious examples of this kind of dynamic is creative thinking
(Boden, 2003; Goertzel, 1997), where the attentional focus continually combinationally creates
new ideas, which are then tested via checking which ones can be validated in terms of (built up
from) existing knowledge.
The analysis stage may result in items being pushed out of the attentional focus, to be
replaced by others. Likewise may the synthesis stage: the combinations may overshadow and
then replace the things combined. However, in human minds and functional AI minds, the
attentional focus will not be a complete chaos with constant turnover: Sometimes the same set of
ideas – or a shifting set of ideas within the same overall family of ideas – will remain in focus for a
while. When this occurs it is because this set or family of ideas forms an approximate attractor
for the dynamics of the attentional focus, in particular for the forward-analysis dynamic of
speculative combination and integrative explanation. Often, for instance, a small “core set” of
ideas will remain in the attentional focus for a while, but will not exhaust the attentional focus:
the rest of the attentional focus will then, at any point in time, be occupied with other ideas
related to the ones in the core set. Often this may mean that, for a while, the whole of the
attentional focus will move around quasi-randomly through a “strange attractor” consisting of
the set of ideas related to those in the core set.
3.4.5 Conclusion
The ideas presented above (the notions of synthesis and analysis, and the hypothesis of self and
attentional focus as attractors of the iterative forward-analysis dynamic) are quite generic and
are hypothetically proposed to be applicable to any cognitive system, natural or artificial. Later
chapters will discuss the manifestation of the above ideas in the context of CogPrime. We have
found that the analysis/synthesis approach is a valuable tool for conceptualizing CogPrime’s
cognitive dynamics, and we conjecture that a similar utility may be found more generally.
Next, so as not to end the section on too blasé of a note, we will also make a stronger
hypothesis: that, in order for a physical or software system to achieve intelligence that is roughly
human-level in both capability and generality, using computational resources on the same order
of magnitude as the human brain, this system must
• manifest the dynamic of iterated synthesis and analysis, as modes of an underlying “selfgenerating
system” dynamic
3.5 Perspectives on Machine Consciousness 51
• do so in such a way as to lead to self and attentional focus as emergent structures that serve
as approximate attractors of this dynamic, over time periods that are long relative to the
basic “cognitive cycle time” of the system’s forward-analysis dynamics
To prove the truth of a hypothesis of this nature would seem to require mathematics fairly
far beyond anything that currently exists. Nonetheless, however, we feel it is important to
formulate and discuss such hypotheses, so as to point the way for future investigations both
theoretical and pragmatic.
3.5 Perspectives on Machine Consciousness
Finally, we can’t let a chapter on philosophy – even a brief one – end without some discussion
of the thorniest topic in the philosophy of mind: consciousness. Rather than seeking to resolve
or comprehensively review this most delicate issue, we will restrict ourselves to giving it in
Appendix ?? an overview of many of the common views on the subject; and here in the main text
discussing the relationship between consciousness theory and patternist philosophy of cognition,
the practical work of designing and building AGI.
One fairly concrete idea about consciousness, that relates closely to certain aspects of the
CogPrime design, is that the subjective experience of being conscious of some entity X, is correlated
with the presence of a very intense pattern in one’s overall mind-state, corresponding to X.
This simple idea is also the essence of neuroscientist Susan Greenfield’s theory of consciousness
[Gre01] (but in her theory, "overall mind-state" is replaced with "brain-state"), and has much
deeper historical roots in philosophy of mind which we shall not venture to unravel here.
This observation relates to the idea of "moving bubbles of awareness" in intelligent systems.
If an intelligent system consists of multiple processing or data elements, and during each (sufficiently
long) interval of time some of these elements get much more attention than others,
then one may view the system as having a certain "attentional focus" during each interval. The
attentional focus is itself a significant pattern in the system (the pattern being "these elements
habitually get more processor and memory", roughly speaking). As the attentional focus shifts
over time one has a "moving bubble of pattern" which then corresponds experientially to a
"moving bubble of awareness."
This notion of a "moving bubble of awareness" ties in very closely to global workspace
theory [Baa97] (briefly mentioned above), a cognitive theory that has broad support from
neuroscience and cognitive science and has also served as the motivation for Stan Franklin’s
LIDA AI system [BF09], to be discussed in Chapter ??. The global workspace theory views the
mind as consisting of a large population of small, specialized processes – a society of agents.
These agents organize themselves into coalitions, and coalitions that are relevant to contextually
novel phenomena, or contextually important goals, are pulled into the global workspace (which
is identified with consciousness). This workspace broadcasts the message of the coalition to all
the unconscious agents, and recruits other agents into consciousness. Various sorts of contexts
– e.g. goal contexts, perceptual contexts, conceptual contexts and cultural contexts – play a
role in determining which coalitions are relevant, and form the unconscious "background" of
the conscious global workspace. New perceptions are often, but not necessarily, pushed into the
workspace. Some of the agents in the global workspace are concerned with action selection, i.e.
with controlling and passing parameters to a population of possible actions. The contents of
the workspace at any given time have a certain cohesiveness and interdependency, the so-called
52 3 A Patternist Philosophy of Mind
"unity of consciousness." In essence the contents of the global workspace form a moving bubble
of attention or awareness.
In CogPrime, this moving bubble is achieved largely via economic attention network (ECAN)
equations [GPI + 10] that propagate virtual currency between nodes and links representing elements
of memories, so that the attentional focus consists of the wealthiest nodes and links.
Figures 3.3 and 3.4 illustrate the existence and flow of attentional focus in OpenCog. On the
other hand, in Hameroff’s recent model of the brain [Ham10], the brain’s moving bubble of
attention is achieved through dendro-dendritic connections and the emergent dendritic web.
Fig. 3.3: Graphical depiction of the momentary bubble of attention in the memory of an
OpenCog AI system. Circles and lines represent nodes and links in OpenCogPrimes memory,
and stars denote those nodes with a high level of attention (represented in OpenCog by
the ShortTermImportance node variable) at the particular point in time.
In this perspective, self, free will and reflective consciousness are specific phenomena occurring
within the moving bubble of awareness. They are specific ways of experiencing awareness,
corresponding to certain abstract types of physical structures and dynamics, which we shall
endeavor to identify in detail in Appendix ??.
3.6 Postscript: Formalizing Pattern 53
Fig. 3.4: Graphical depiction of the momentary bubble of attention in the memory of an
OpenCog AI system, a few moments after the bubble shown in Figure 3.3, indicating the moving
of the bubble of attention. Depictive conventions are the same as in Figure 1. This shows
an idealized situation where the declarative knowledge remains invariant from one moment to
the next but only the focus of attention shifts. In reality both will evolve together.
3.6 Postscript: Formalizing Pattern
Finally, before winding up our very brief tour through patternist philosophy of mind, we will
briefly visit patternism’s more formal side. Many of the key aspects of patternism have been
rigorously formalized. Here we give only a few very basic elements of the relevant mathematics,
which will be used later on in the exposition of CogPrime. (Specifically, the formal definition of
pattern emerges in the CogPrime design in the definition of a fitness function for “pattern mining”
algorithms and Occam-based concept creation algorithms, and the definition of intensional
inheritance within PLN.)
We give some definitions, drawn from Appendix 1 of [Goe06a]:
Definition 1 Given a metric space (M, d), and two functions c : M → [0, ∞] (the “simplicity
measure”) and F : M → M (the “production relationship”), we say that P ∈ M is a pattern
in X ∈ M to the degree
54 3 A Patternist Philosophy of Mind
ι P X =
((
1 −
d(F (P), X)
c(X)
) c(X) − c(P)
c(X)
This degree is called the pattern intensity of P in X. It quantifies the extent to which P
is a pattern in X. Supposing that F (P) = X, then the first factor in the definition equals 1,
and we are left with only the second term, which measures the degree of compression obtained
via representing X as the result of P rather than simply representing X directly. The greater
the compression ratio obtained via using P to represent X, the greater the intensity of P as a
pattern in X. The first time, in the case F (P) ≠ X, adjusts the pattern intensity downwards to
account for the amount of error with which F (P) approximates ≠ X. If one holds the second
factor fixed and thinks about varying the first factor, then: The greater the error, the lossier
the compression, and the lower the pattern intensity.
For instance, if one wishes one may take c to denote algorithmic information measured on
some reference Turing machine, and F (X) to denote what appears on the second tape of a
two-tape Turing machine t time-steps after placing X on its first tape. Other more naturalistic
computational models are also possible here and are discussed extensively in Appendix 1 of
[Goe06a].
) +
Definition 2 The structure of X ∈ M is the fuzzy set St X
function
χ StX (P) = ι P X
defined via the membership
This lets us formalize our definition of “mind” alluded to above: the mind of X as the set
of patterns associated with X. We can formalize this, for instance, by considering P to belong
to the mind of X if it is a pattern in some Y that includes X. There are then two numbers
to look at: ι P X and P (Y |X) (the percentage of Y that is also contained in X). To define the
degree to which P belongs to the mind of X we can then combine these two numbers using
some function f that is monotone increasing in both arguments. This highlights the somewhat
arbitrary semantics of “of” in the phrase “the mind of X.” Which of the patterns binding X to
its environment are part of X’s mind, and which are part of the world? This isn’t necessarily
a good question, and the answer seems to depend on what perspective you choose, represented
formally in the present framework by what combination function f you choose (for instance if
f(a, b) = a r b 2−r then it depends on the choice of 0 < r < 1).
Next, we can formalize the notion of a “pattern space” by positing a metric on patterns, thus
making pattern space a metric space, which will come in handy in some places in later chapters:
Definition 3 Assuming M is a countable space, the structural distance is a metric d St
defined on M via
d St (X, Y ) = T (χ StX , χ StY )
where T is the Tanimoto distance.
The Tanimoto distance between two real vectors A and B is defined as
T (A, B) =
A · B
‖A‖ 2 + ‖B‖ 2 − A · B
and since M is countable this can be applied to fuzzy sets such as St X via considering the
latter as vectors. (As an aside, this can be generalized to uncountable M as well, but we will
not require this here.)
3.6 Postscript: Formalizing Pattern 55
Using this definition of pattern, combined with the formal theory of intelligence given in
Chapter 7, one may formalize the various hypotheses made in the previous section, regarding
the emergence of different kinds of networks and structures as patterns in intelligent systems.
However, it appears quite difficult to prove the formal versions of these hypotheses given current
mathematical tools, which renders such formalizations of limited use.
Finally, consider the case where the metric space M has a partial ordering < on it; we may
then define
Definition 3.1. R ∈ M is a subpattern in X ∈ M to the degree
∫
κ R P∈M
X =
true(R < P )dιP X
∫
P∈M dιP X
This degree is called the subpattern intensity of P in X.
Roughly speaking, the subpattern intensity measures the percentage of patterns in X that
contain R (where "containment" is judged by the partial ordering <). But the percentage is
measured using a weighted average, where each pattern is weighted by its intensity as a pattern
in X. A subpattern may or may not be a pattern on its own. A nonpattern that happens to
occur within many patterns may be an intense subpattern.
Whether the subpatterns in X are to be considered part of the "mind" of X is a somewhat
superfluous question of semantics. Here we choose to extend the definition of mind given in
[Goe06a] to include subpatterns as well as patterns, because this makes it simpler to describe
the relationship between hypersets and minds, as we will do in Appendix ??.

Chapter 4
Brief Survey of Cognitive Architectures
4.1 Introduction
While we believe CogPrime is the most thorough attempt at an architecture for advanced AGI,
to date, we certainly recognize there have been many valuable attempts in the past with similar
aims; and we also have great respect for other AGI efforts occurring in parallel with Cog-
Prime development, based on alternative, sometimes overlapping, theoretical presuppositions
and practical choices. In most of this book we will ignore these other current and historical
efforts except where they are directly useful for CogPrime – there are many literature reviews
already published, and this is a research treatise not a textbook. In this chapter, however, we
will break from this pattern and give a rough high-level overview of the various AGI architectures
at play in the field today. The overview definitely has a bias toward other work with
some direct relevance to CogPrime, but not an overwhelming bias; we also discuss a number of
approaches that are unrelated to, and even in some cases conceptually orthogonal to, our own.
CogPrime builds on prior AI efforts in a variety of ways. Most of the specific algorithms
and structures in CogPrime have their roots in prior AI work; and in addition, the CogPrime
cognitive architecture has been heavily inspired by some other holistic cognitive architectures,
especially (but not exclusively) MicroPsi [Bac09], LIDA [BF09] and DeSTIN [ARK09a, ARC09].
In this chapter we will briefly review some existing cognitive architectures, with especial but
not exclusive emphasis on the latter three.
We will articulate some rough mappings between elements of these other architectures and
elements of CogPrime – some in this chapter, and some in Chapter 5. However, these mappings
will mostly be left informal and very incompletely specified. The articulation of detailed interarchitecture
mappings is an important project, but would be a substantial additional project
going well beyond the scope of this book. We will not give a thorough review of the similarities
and differences between CogPrime and each of these architectures, but only mention some of
the highlights.
The reader desiring a more thorough review of cognitive architectures is referred to Wlodek
Duch’s review paper from the AGI-08 conference [DOP08]; and also to Alexei Samsonovich’s
review paper [Sam10], which compares a number of cognitive architectures in terms of a feature
checklist, and was created collaboratively with the creators of the architectures.
Duch, in his survey of cognitive architectures [DOP08], divides existing approaches into three
paradigms – symbolic, emergentist and hybrid – as broadly indicated in Figure 4.1. Drawing on
his survey and updating slightly, we give here some key examples of each, and then explain why
57
58 4 Brief Survey of Cognitive Architectures
CogPrime represents a significantly more effective approach to embodied human-like general
intelligence. In our treatment of emergentist architectures, we pay particular attention to developmental
robotics architectures, which share considerably with CogPrime in terms of underlying
philosophy, but differ via not integrating a symbolic “language and inference” component such
as CogPrime includes.
In brief, we believe that the hybrid approach is the most pragmatic one given the current state
of AI technology, but that the emergentist approach gets something fundamentally right, by
focusing on the emergence of complex dynamics and structures from the interactions of simple
components. So CogPrime is a hybrid architecture which (according to the cognitive synergy
principle) binds its components together very tightly dynamically, allowing the emergence of
complex dynamics and structures in the integrated system. Most other hybrid architectures are
less tightly coupled and hence seem ill-suited to give rise to the needed emergent complexity. The
other hybrid architectures that do possess the needed tight coupling, such as MicroPsi [Bac09],
strike us as underdeveloped and founded on insufficiently powerful learning algorithms.
Fig. 4.1: Duch’s simplified taxonomy of cognitive architectures. CogPrime falls into the “hybrid”
category, but differs from other hybrid architectures in its focus on synergetic interactions
between components and their potential to give rise to appropriate system-wide emergent structures
enabling general intelligence.
4.2 Symbolic Cognitive Architectures
A venerable tradition in AI focuses on the physical symbol system hypothesis [New90], which
states that minds exist mainly to manipulate symbols that represent aspects of the world or
themselves. A physical symbol system has the ability to input, output, store and alter symbolic
entities, and to execute appropriate actions in order to reach its goals. Generally, symbolic
cognitive architectures focus on “working memory” that draws on long-term memory as needed,
and utilize a centralized control over perception, cognition and action. Although in principle
such architectures could be arbitrarily capable (since symbolic systems have universal repre-
4.2 Symbolic Cognitive Architectures 59
sentational and computational power, in theory), in practice symbolic architectures tend to be
weak in learning, creativity, procedure learning, and episodic and associative memory. Decades
of work in this tradition have not resolved these issues, which has led many researchers to
explore other options. A few of the more important symbolic cognitive architectures are: