EVOLUTION-THEORETIC APPROACH TO SYNTHETIC STUDY OF INTELLIGENCE
Hideyuki Nakashima
Future University Hakodate
116-2 Kamedanakano-cho, Hakodate, Hokkaido, Japan 041-8655(Received December 18, 2010; Accepted December24, 2010)(Abstract)
This paper mainly talks about researchmethodology of synthetic sciences. First, I compare
analytic and synthetic sciences and point out thedifference of the view point, or the standing point ofthe researcher. Analytic sciences require exo-systemview and methodology, while constructive orsynthetic sciences require endo-system view andmethodology. To study intelligence, we need aconstructive methodology with internal observation(endo-system view). Then, I focus on themethodology of synthetic sciences and point out thatthe essential driving force of a synthetic methodology
is the evolutionary method. It is a loop of generationand selection. I formalize the loop of synthetic
methodology that includes analysis as its part. Finally,
I layout my current research plan to implement a
multi-level emergent system using evolutionary
method.

simpler subsystems and recursively analyze them till
we understand every subsystems and the structure of
their connection. It is generally understood thatanalysis is from whole to parts, and synthesis isreciprocally from parts to whole [2] (Figure 1). In thisview, synthesis is (wrongly) understood as a similar
process as building a plastic model from its parts. I
claim this is not the case. Synthesis is a much harder
process. It is not building a given plastic model, butrather designing those parts of the model of yet
non-existing object. In synthesis, the parts are not
identified unless we know the whole. And the wholecannot be constructed unless we have the parts. The
only way out of this deadlock is to run a loop, justlike chicken-egg problem - we cannot identify either
chicken or egg as an origin, but they both exist.
How can we get out of this vicious loop? Well,the solution lies in that loop itself. Before we propose
the solution however, we have to introduce one moreconcept: The viewpoints of systems.

(Keywords)
constructive science, artificial intelligence, FNS,
endo-system view, evolutionary method, mind-bodyproblem
1. Constructive vs. analytical sciences
The research of AI (Artificial Intelligence)seeks for the definition of intelligence. One of thetargets of AI is to understand the concept ofintelligence in the abstract level. It does notnecessarily mirror human intelligence. To this end,
the most promising and thus commonly used method
is to construct a program that exhibits intelligent
behaviors. The prominent property of AI research isconstruction of programs. Analysis of human
intelligence in AI is common with psychology andcognitive science. In this respect, AI is characterized
as constructive science.
Constructive science needs a different
methodology from analytical science. Study ofintelligence needs its own research methodology. For
example, behaviorism in experimental psychology,
which followed the methodology of natural sciences,
did not succeed to expose the essential part ofintelligence. Intelligence cannot be understood fromits outside behavior alone. Cognitive Science wasborn then to talk about internal processes – our own
thoughts and representations. In this sense, theresearch area of AI can be considered as part ofCognitive Science. AI is significant in its use ofcomputer programs as research tool. It needs
observation from inside [1]. Observation from inside
affects the system being observed. This interaction
violates the objective requirement of natural sciences.
We need another paradigm. The basic methodologyof analytical science is to divide a system into its
Viva Origino 39 (2011) 1 - 5© 2011 by SSOEL Japan

Figure 1 A wrong view of synthesis as a reciprocal
process of analysis

2. Needs of the endo-system view
AI researchers are interested in emergence ofintelligence (for example see [3,4]). We write aprogram for what we think the basic mechanismunderlying some intelligent behavior. When theprogram runs, and some intelligent behavior emerges,
we analyze it to study more about intelligence. Whenthe program shows no interesting behavior, wechange the program. In this sense, the program does
not run independently from the researcher who
designed and wrote the program. There is a constantinteraction between the program and the researcher.
Therefore, it should be said that research of Cognitive
Science and AI includes researchers, a program andthe environment of the execution of the program. In
other words, researchers are in the loop. Since theresearcher is inside the system, it is said that (s)he isat the endo-system view point.
There are Exo and Endo-System Views [1].
Both are important but should not be confused eachother. Exo-system view puts the observer outside of
the system being observed (Figure 2 left). Naturalsciences, physics in particular, presupposes this view.
Although observation affects the system beingobserved in principle, natural sciences tries to-1-Viva Origino 39 (2011) 1 - 5

minimize the interferences. However, there aresystems that can be observed only by participating in
the process. This is the endo-system view (Figure 2right). There are trade-offs between objectivity ofexo-system view and observability of endo-systemview.
Atsunobu Ichikawa claimed that different
cultures have different world views [5]. Western viewof the world is from God's eyes view and imposes
total consistency of the world. Note that this is also
the view of the natural science. Japan, on the other
hand allow/accept different rules for different groups.
We see a group, such as a family and a company,
from inside as a member.
Yoshihiko Ikegami reported that the view-pointof constructing and understanding sentences aredifferent between Japanese and English [6]. A typicalexample is illustrated using Yasunari Kawabata's“Snow Country”. In understanding the first sentence“国境の長いトンネルを抜けると雪国であった”,
the reader/hearer identifies himself/herself with theperson on the train (Figure 3 right). In understanding
its English translation by E. Seidensticker “The traincame out of the long tunnel into the snow country” on
the other hand, the reader/hearer is outside the tunnel,
probably located in the sky, and watching the train
coming out of the tunnel (Figure 3 left). The original
Japanese sentence lacks grammatical subject, whichis the train in the English version. Japanese readers
fill the information with their commonsense.
Takehiro Kanaya takes the same example and claims
that Japanese is expressed from insects-eyes-view
while English is expressed from birds-eyes-view [7].
Bin Kimura claims that Japanese primary viewof world the world is as koto (events), in contrast tomono (things) [8]. We can see an object, for example
an apple, as mono that exists outside in the reality.
But we can also experience an apple falling as koto.
In the latter case, the person experiencing the event isincluded in the event. In other words, Western view
detaches human from nature and sees the world as anobject while Eastern view puts human in the natureand experience the world. We see essentially thesame phenomena as Snow Country example here.
3. Loop of synthesis methodology
As stated before, it is generally understood thatanalysis and synthesis are reciprocal activities.
Analysis divides the whole into parts, and studies theindividual parts and their interrelationships. Synthesis
assembles the whole from parts. This rather simplistic
view is based on the image of disassembly andreassembly. This may sound obvious when we think
about plastic model kits. When we buy a kit, thewhole set of parts are prepared and we use all of themand connect them together. However, the reality isdifferent. First of all, when we design a new system,
necessary parts are not known yet. Synthesis must
start from the identification of the parts. However,parts may not be identified until we know the outputof the synthesis, the whole system. So, there must bea loop from the parts to the whole and the whole tothe parts. Only after we have enough knowledge on
the new system, we can identify necessarycomponents and make it a routine work. To get
knowledge of the system, we have to analyze thesystem. Analysis must be a part of synthesis.
Artificial Life [9] is a typical synthetic field. For
example, a simulation of a flock of birds [10] is
generated from a small number of local rules on theflight of each bird. This simulation is not based on
the analysis of actual flight of birds. It is created byadjusting local rules until desired flight pattern isrealized. I will formalize this kind of activity.
As an example, I will take early days of airplanedevelopment. Although an airplane is a too simple
artifact compared to complex multi-level systems weare interested in, it nevertheless gives us a typicalexample of synthetic loop.
In the beginning, well before Wright brothers'
first successful flight, there were many failures. Therequirement was initially, I think, very blur andsimple: To make a machine fly. Some physicalequipment was build according to the requirement.
Then it was tested. The result was then analyzed tofind out the reason of failure (or success). The resultof analysis is then fed back to the requirement
specification. This loop repeats with new requirement
and continues until safe flight is achieved - the loop isstill continuing currently.
The study of aerodynamics was developed
while running the above synthetic loop. It provided astrong analytic tool for designers. Human factors andcognitive psychology were involved later.
The specifications (goals) may change whilerunning a synthetic loop. This point signifies thedifference between our synthetic methodology andtraditional engineering, where the goal is fixed inprior to the initiation of designing the system. Since
the goal, and therefore the evaluation criteria of thesystem, changes dynamically, analytical methodology
must also change. In the sense that an analyticalprocedure can be determined only after the actual
construct is generated, analysis and synthesis are notsimply activities in opposite directions, but can beconsidered to be orthogonal to each other.

Figure 2 Exo-system view (left) and endo-system view(right)Figure 3 Exo-system view (left) and endo-system view(right) of a train (Drawing by the author)-2-Viva Origino 39 (2011) 1 - 5

Figure 4 is a formalization of synthetic processdescribed above, called FNS (Future NoemaSynthesis) diagram. Circles designate concepts orobjects and arrow designate human action to makethe transition.
-into several entities on the lower level, forming
part-of hierarchy. If a Noesis in certain level
corresponds to a brain, for example, it is decomposedinto bunch of neurons in the lower level. The itemthat is an external environmental factor in the lowerlevel (right side) is internalized in the Noesis in theupper level (left side). That is, the system thatconsists of Noesis (in the center) and other elements(distributed in the environment) on the lower level(right side) become either the central Noesis or one
element of the environment in the upper level (leftside).
On the other hand, Noema take on adifferent description system. For example, organlevel Noema and cellular level Noema form
independent systems. Sometimes we can find verticalcausal relationships among those Noema.

Future Noema: Goal, or initial plan of aflying machine.
C1: Synthesis of a system. (Experimentalairplane or a model of it).
Noesis: an actually build airplane.
C1.5: Interaction with the environment: aflight test.
Analysis:Analysis of the result of theinteraction - flight.
Current Noema: Analyzed characteristics ofthe flight
C3: Focusing. To feedback the analysis to thenext model, the designer has to focus onparticular factors.

4. Evolutionary method
The FNS-diagram tells us the following: Wehave to repeat a synthetic loop to approach our goal,i.e., future Noema [11]. However, the goal itself maychange during the process. The process itself forms aTerms Noema and Noesis were originallyconceived by Edmund Husserl, and Kimura [11] uses
them with slightly different meaning. Since it is very
difficult to explain them simply, please refer to thesource, or consider them as mere symbols in thisarticle. A Noema is like a concept or plan. A Noesis
is actualization of Noema.
The most important transition here is theinteraction of Noesis and the environment. There arefactors that cannot be controlled directly by thedesigner, such as wind and humidity. Almost all
activities of synthesis involve such uncontrollableinteractions. Traditionally, these were not considered
important, but synthesis is very difficult because of
the unpredictable interaction with the environment.
For more detailed description of Noesis and Noema,
refer to other articles [12,13].
I extend the picture to synthesis of multi-levelsystems and get a multi-level FNS diagram (Figure 5).
The figure shows the hierarchy of three levels higher levels are placed on the left. Those three levels
are connected only by the relationship of Noeses.
One Noesis in the higher level (left) is decomposed

Figure 4 FNS diagram of a synthetic loop

Figure 5 Multi-level FNS diagram-3-Viva Origino 39 (2011) 1 - 5complex and holistic system. We cannot directly
control complex interactions. I claim that theevolutionary method is the key here.
The simplified, yet essential, part of evolution
process (Figure 6) consists of the following twoelements:

may not be a good idea because directional selection
may filter out some latent future possibilities
In summary, evolutionary method is the only
approach to run the synthetic loop as formalized as
our FNS diagram. There are other partially applicable
improvement methods, but no better almighty
alternatives.

1. Generation of variants
2. Selection (note: criteria changes dynamically)
There are two possible ways to generate variants.
One is improvement of the current model, and theother is random change. The former is frequently
used in engineering. Various optimization methods
including hill-climbing search are useful here.
However, those methods are bound within local
maximums and cannot jump out of it. Evolution is thelatter – random change. Mutation and crossover ofgenes generates a new variation randomly. Most ofrandom generation fails but yet includes a newpossibility. Selection mechanism filters out goodcandidates. Wikipedia describes Genetic Algorithm,
which is an instance of the evolution processdescribed above, as follows:

Figure 6 The essential part of evolution process

5. The mind-body problem
A human being must be understood inmulti-levels. Those levels include mind (the functionof the brain) and the body (the brain, or the structured
collection of neural cells) [15]. How is the mind
encoded as behavior of neural cells? For example,
how can the decision of “raising the right hand”
trigger the series of firing of neural cells thateventually send signals to proper muscles of the righthand to cause it rise? Or is it the other way around?
Is it the case that certain patterns of neural firing are
affecting both the right hand and the cognition of theintention of raising the right hand? In other words,
the firing pattern of neurons is the cause and all othercognitive functions are just the result? It is quite
unlikely. They both are real. Then how do theyinteract with each other?
The answer here is the evolution. If we connect
billions of neurons in arbitrary way, it does not
function as desired. It is quite unlikely they represent
a coherent mind. Only a certain connection, born as
the result of long evolution, can support (or emerge)
cognitive function. The answer to the mind-bodyproblem is “it just happens to be in a configuration sothat mind and body correspond to each other”. Bothlevels, mind and body, function independently. Butthe body is configured in a very specific way, again
through evolution, to correspond to the function ofmind. Otherwise, a human being did not survive theevolution process.
The above explanation is just a conjecture withno proven ground. How can we prove this? One
place to begin is to show that synthesis of multi-levelsystem is actually possible, and then study the detailsof the mechanism. I will illustrate my trial (just a planyet) in the next section.

1. Choose initial population.
2. Evaluate the fitness of each individual in thepopulation.
3. Repeat until termination: (time limit orsufficient fitness achieved).
3-1. Select best-ranking individuals to reproduce.
3-2. Breed new generation through crossover
and/or mutation (genetic operations) and givebirth to offspring.
3-3. Evaluate the individual fitness’s of theoffspring.
3-4. Replace worst ranked part of population withoffspring.
According to Ichikawa [14], a general process of everchanging systems with the following conditions:-Existence of self-replication unit (genome) tomaintain regularityExistence of a system structure ofself-replication units (existence of elements
and a system that connects those elements)
Possibility for mutation of the system
structure
Interaction (competition) among replicatorsystems (for frequency of replication)
Existence of external environment

Although evolutionary process gives us a guide to run
a synthesis loop, the difference between synthesis andevolution should also be noted. Natural evolution
process does not have any intention or direction.
Evolution occurs as a result of selection by nature.
Whatever variant that happen to leave manyoffspring’s survive. Therefore, the concept ofevolution does not include improvement. But oursynthesis of artifacts is an intentional process. For
example, optimization method may be applicable
locally. Selections can also be directional, but this6. My current research plan
I am currently working on a system that exhibits
multi-level emergence. In cellular automaton
researches, only single level emergences weredetected. The detections were by human observers.
Researchers run experiments with various initial-4-Viva Origino 39 (2011) 1 - 5

settings and reported interesting outcomes. Famous
patterns in Life Game such as gliders and glider guns
are fond in a similar way.
I want a mechanism that automatically fixes any
interesting emergent structure. In the real world, theemergence of a cell structure of living organisms
were fixed by the self copying ability of the cells. No
outsider is requested for the process is purely
automatic. I want to see the similar automatic fixationof some emergent property, and see the second levelemergence based on the first level emergent structureused as new building blocks.
An image is given in Figure 7. The figure
depicts the second level emergence. The first levelemergence is a structure of connecting cells toenclose a small area – just like real cells that holdinternal environment different from outside. Those
structures are then used as building blocks for thesecond level emergence, a similar structure in thiscase to surround larger area. The important point isthat there is no human interaction during the process.
The formation must be purely automatic. To
implement this, I believe there must be some added
factor other than conventional cellular automaton.
For example, the introduction of energy or food helps,
because those may be accumulated in the enclosed
area and make the surrounding cells more stable.
Study of minimum synthetic life [16] may also help.
In summary, we need the following elements:- Emergence of the upper level structure- Feedback from the upper level structure to thelower level selection (adaptability)- Fixation of the emerged structure
I plan to use - Cellular Automaton withevolutionmechanism.
Ichikawa
introduced
evolutionary CA [5]. It is evolutionary because thestate of cells may randomly change during theprocess. In my case, evolution means changing not
only the state of cells but also rewriting rules and/orselection (survival) rules while running the system.
This means that we are forming a meta-level
evolutionary system – the rule of the system itself
evolves.
The rewriting rule of a cellular automaton canbe coded into a simple transition table from states ofan individual cell and its adjacent cells to the next
state of the cell in question. A simple CA such as Life
Game can be coded by 10 bits (1 cell state + 8
adjacent cell states + next sell state, all binary). If we
use those 10 bits as the state of a cell, it can be used
to compute the next state of the cell itself. We have toadd some categorization of those 10 bits into 0-1 stateof live or dead. Otherwise, the rewriting tablebecomes infinitely large.
We need to add some mutation to the state ofcells, and add some selection mechanism, and thenthe automaton becomes an evolving system. We do
not have to try many different initial configurations
because mutation takes care of it. Once the system isimplemented, we can just run the system and wait forsome interesting emergent property to be fixed by thesystem itself.
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