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Systems Intelligence:
Splash slide
Theory and Application
World Congress / ISSS 2000
Toronto, Canada
July 16-22, 2000
What is Life? Special Integration Group
Jonathan R.A. Maier, Graduate Assistant
Department of Mechanical Engineering
College of Engineering and Sciences
Clemson University, Clemson, SC 29634 USA
Systems Intelligence
Jonathan R.A. Maier
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Differing Perspectives on Intelligence
Previously, many different disciplines have had their own
perspectives on intelligence, including…
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Neuroscience
Psychology
Philosophy
Business Management
Artificial Intelligence
Artificial Life
Systems Intelligence Theory attempts to integrate the
differing perspectives on intelligence from each of these
disciplines.
Systems Intelligence
Jonathan R.A. Maier
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Driving Questions
• What makes some systems, like humans, intelligent?
• And in what sense may other systems be intelligent?
• How can intelligence be measured in general?
• What are the common structural factors involved?
• How can these factors be manipulated?
• What is the connection between intelligence and life?
Systems Intelligence Theory is an attempt to answer
these questions within one coherent framework.
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Jonathan R.A. Maier
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Lessons from Neuroscience
It is almost universally agreed that the animal brain is the
seat of animal intelligence…
• Animal intelligence emerges from the combined interaction of a
relatively large group neurons
• These neurons are relatively primitive
• The neurons are interconnected on a rather large scale
• Neurons communicate only along one way channels
• Neurons communicate at relatively low speeds
• More sophisticated intelligence is related to the size of the brain and
the number of layers of the brain.
• However a brain in and of itself is not sufficient for intelligence
• The animal body and external environment are also necessary
Systems Intelligence
Jonathan R.A. Maier
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Lessons from Artificial Neural Networks
Artificial neural networks have been shown to display
many sophisticated animal behaviors, including…
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Learning
Pattern recognition
Speech synthesis
Medical diagnosis
Decision making
Signal processing
Dreaming
Thus artificial neural networks provide empirical evidence that the
basic understanding of animal intelligence from neuroscience is
correct.
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Jonathan R.A. Maier
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Lessons from Artificial Intelligence
The “Bottom-Up” Theory of Artificial Intelligence is
grounded on four basic principles:
• Situatedness
• Embodiment
• Emergence
• Subjectivity
Using these ideas, robots have been built modeled after insects which
are capable of learning how to walk and explore a real environment.
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Jonathan R.A. Maier
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Lessons from Organizational Learning
Researchers in the business community have found
that groups usually have a lower Intelligence Quotient
(IQ) that the individual IQ’s of their members.
Some strategies have been
identified for improving
organizational intelligence,
based upon organization
learning.
Systems Intelligence Theory provides an explanation
of how and why some groups are more intelligent than
others, and what can be done about it
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Jonathan R.A. Maier
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A Systems Theory of Intelligence
“Intelligence emerges as the result of the combined
interaction between a group of distinct but interconnected
agents operating within a larger environment that is
perceived through sensors.”
• Intelligence is not programmed but rather emerges when
a proper structure is in place
• Intelligence depends not on the individual agents but
rather on the framework in which they may interact and
their external environment
• Intelligence emerges from the interaction between agents
but develops through interaction with the environment
Systems Intelligence
Jonathan R.A. Maier
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What is an Agent?
“An agent is a system that is capable of 1) accepting one
or more inputs, 2) making one or more decisions based
upon that input, 3) exporting that decision as one or
more outputs, and 4) being changed in the process.”
• Note that a “drone” is similar to an agent, except that a drone is
not changed in the process of making a decision
• Some examples of agents include…
• Biological neurons
• Artificial neurons
• People
• Bacteria
Note that some systems can act either as agents or drones
depending on the situation.
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Jonathan R.A. Maier
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Key Structural Factors: Agent Factors
• Number of Agents
• Skill of Individual Agents
• Speed of Agent Communication
• Distance of Agent Communication
• Frequency of Agent Communication
• Quality of Agent Communication
• Mutability of Agents
• Level of Agent Interconnectedness
• Number of Layers of Agents
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Jonathan R.A. Maier
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Key Structural Factors: Environmental Factors
• Complexity of the Environment
• Number of Sensors in the Environment
• Number of Variables that may be Sensed from the Environment
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Jonathan R.A. Maier
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Qualitative Measures of System Intelligence
Intelligence may be measured qualitatively by…
• Goaling: the ability to recognize and/or pursue goals
in the external environment
• Creativity: the ability to generate actions to take in
the external environment, and the ability to take
actions in the environment
• Learning: the ability to correlate correctly
subsequent changes in the environment with causes
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Jonathan R.A. Maier
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Quantitative Measure of System Intelligence
Intelligence may be measured quantitatively by…
n
1
*
SCIN    xi
n i 1
(arithmetic mean with equal weights)
where SCIN = Structural Capacity for Intelligence, Normalized,
n = the number of key structural factors, xi* = normalized values for
key structural factors.
Systems Intelligence
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SCIN
Examples of Intelligent Systems
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.66
Humans
0.63
Internet
0.59
Large
Business
0.57
Bugbots
0.57
A-Design
0.52
0.5
City Traffic Ant Colony
0.44
Bacteria
• The Structural Capacity for Intelligence of a variety of systems
(biological, social, and artificial) based upon key structural factors
alone has been analyzed
• While the SCIN metric lacks sensitivity, the relative intelligence
measured for each system is intuitively correct
Systems Intelligence
Jonathan R.A. Maier
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Systems Intelligence Theory and the Question of Life
• What makes some systems, like humans, intelligent?
• Answer: Proper structural framework in which agents interact
• And in what sense may other systems be intelligent?
• Answer: Same. The key is agents vs. drones
• How can intelligence be measured, in general?
• Answer: Qualitative metrics and SCIN metric
• What are the common structural factors involved?
• Answer: Key structural factors identified
• How can these factors be manipulated?
• Answer: Which factors may be manipulated varies case by case
Now can Systems Intelligence Theory be used to
answer the question “What is Life?”
Systems Intelligence
Jonathan R.A. Maier
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The Question of Life: Biological Systems
Observations from applying systems intelligence theory
to biological systems:
• Intelligence has been shown and quantified in the
most simple biological systems (bacteria) to the highly
complex (humans)
• Similar arguments can be used for any biological
system
• Hence it becomes apparent that the same complex
structures that are necessary for life are sufficient for
intelligence
Alternatively, we can say that the key structural factors that are
necessary for systems intelligence are necessary for life
Systems Intelligence
Jonathan R.A. Maier
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The Question of Life: Social Systems
Observations from applying systems intelligence theory
to social systems:
• Some social systems (e.g., businesses, families,
governments) are sometimes considered to be alive
while others (e.g., traffic systems) are not
• However all social systems consist of living things
• Hence social systems tend to exhibit characteristics of
living systems
• Systems intelligence theory tells us that all these
social systems exhibit the phenomenon of intelligence,
which they share with all biological systems
Hence Systems Intelligence Theory strengthens the argument that
social systems are alive
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Jonathan R.A. Maier
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The Question of Life: Artificial Systems
Observations from applying systems intelligence theory
to artificial systems:
• Much recent effort in the field of Artificial Life has gone into
answering the questions of how to model, simulate, and
create artificial life
• Research in Artificial Life has much in common with the
“bottom-up” school of AI in that hardware or software
networks with relatively simple programming are used to
simulate living processes
• Systems intelligence theory tells us what these artificial
systems have in common with obviously living (biological)
and arguably living (social) systems, which are the key
structural factors necessary for systems intelligence
It may be concluded, therefore, that artificial systems that exhibit
intelligence can be classified as living
Systems Intelligence
Jonathan R.A. Maier
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The Question of Life: Agents vs. Drones
Recall that only systems composed of agents, not
drones, are classified as intelligent.
• Thus only artificial systems composed of agents can be
classified as intelligent and hence living
• Moreover, most artificial systems (i.e., machines) are not
composed of agents and hence are neither intelligent nor
alive
• Similarly, only natural systems in situations where they
behave as agents can be classified as living
These considerations lead to a novel definition of life itself…
Systems Intelligence
Jonathan R.A. Maier
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The Question of Life: A Novel Definition
“Intelligence is a necessary and sufficient condition for
life. Hence, any intelligent system is alive and every
living system has intelligence. In other words, the key
structural factors that are necessary for intelligence are
sufficient for life.”
Intuitive Appeal:
• All biological systems are demonstrably intelligent and therefore alive
• Social systems also show intelligence and are therefore alive
• But only as long as the constituent members are “together”
• Only artificial systems composed of agents show intelligence and are
therefore alive
• Thus excluding everyday machines, which intuitively are not alive
• Avoiding panvitalism
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Jonathan R.A. Maier
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The Question of Death: A Novel Definition
“When the agents in an intelligent system cease
communicating and/or become immutable, the system
ceases to be intelligent and as such is dead.”
• Note the use of the word “when” implies a temporal dimension
• In biological systems death is when the organism ceases
intercommunicating
• The same is true of an A-Design program, or a bugbot when switched “off”
• A social system (e.g., a business) dies when the members quit
communicating, not when the individual members die
Systems Intelligence
Jonathan R.A. Maier
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Systems Intelligence and the Systems World View
A large portion of the systems for which general systems
theory is useful in understanding show intelligence.
• Often we are interested in increasing (sometimes
decreasing) the level of intelligence of these systems
• Systems intelligence theory identifies the key structural
factors that may be manipulated
• By including artificial systems configured as agents, systems
intelligence theory even extends the range of systems that
may be described by general systems theory
• This leads to an extension of the systems world view…
The world may be viewed as composed (although not exclusively) of
systems with intelligence. Since the intelligence of these systems is
structural in nature, the intelligence of these systems may be
intentionally increased or decreased.
Systems Intelligence
Jonathan R.A. Maier
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The Question of Life: Affordance
What is Affordance?
• Affordance is a fundamental concept in design that subsumes
the concept of function.
• Affordance is what something is good at. For example...
• Buttons afford pushing
• Wheels afford rolling
• Signs afford reading
• Chairs afford sitting
• Note that a system can afford more than one thing
• Note the reflexive character of a system’s affordance
• Note the relationship between “affordance” in design in
general and “embodiment” in artificial intelligence
The concept of affordance can be applied to an elegant
understanding of organisms and life itself...
Systems Intelligence
Jonathan R.A. Maier
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The Question of Life: Organisms
“A system is an organism if, and only if, it affords life.”
Contrast Robert Rosen’s similar statement that “a material
system is an organism if, and if, it is closed to efficient causation.”
Q. How does a system afford life (or equivalently, afford intelligence)?
A. System composed of agents (not all drones), and
Key structural factors in place (and working).
Q. What is life?
A. Intelligent behavior arising from an intelligent system.
Note that the concept of affordance eliminates the need to specify
life as pertaining only to material systems.
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Jonathan R.A. Maier
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Closing Remarks
A systems theory of intelligence has been presented
along with metrics to qualify and quantify the level of
emergent intelligence in any system.
• Has been applied to biological, social, and artificial systems
• Numerical results match our qualitative expectations
• Understanding key structural factors shows how the
intelligence of a system may be increased or decreased
• Has led to a richer understanding of life (and death)
• Forms a useful complement to general systems theory
However, much future work is left to be done….
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Jonathan R.A. Maier
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Future Work
Many research avenues exist for future work with
systems intelligence theory, including…
• Development of a metric with more sensitivity and accuracy
• Determining relative importance of each structural factor
• Identification of other key structural factors (if any)
• Application to more systems
• Better explanation of emergence of intelligence
• Systems theory of stupidity
• Application to distributed vs. collocated teams of people
Individuals interested in pursuing any of these topics are invited to
contact the author.
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Jonathan R.A. Maier
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Opportunity for Dialogue
Questions?
Comments?
Concerns?
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Jonathan R.A. Maier
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