STRUCTURED APPROACH TO THE INTELLIGENT SYSTEM

DESIGN

Dr. Leonid M. Polyakov

Professor of the Department of Computer Systems and Math

Globe Institute of Technology

New York, NY

Leonid@globeinstitute.org

ABSTRACT

Artificial Intelligence (AI) is a science of intelligence system

design. Existing definitions of intelligence don’t answer some

important questions of engineering procedures. What kinds of

intellectual tasks do we have? Who is more intelligent or smarter: a

scientist or a wood-maker (human or machine), a metal-maker or a

wood-maker? How to design a system with reasoning as the most

powerful intellectual function? What is intuition? Can we design a

system with intuition?. All these topics are subjects of discussion in

this paper. The goal of this paper is to find active, productive may

be not the best way to determine the starting position and some

directions of intelligent system design.

Keywords: intelligence, intuition, associative thinking, fuzzy,

agent classes, intelligence classes, structure, reasoning,

preposition logic, predicate logic, knowledge base, rules of

reasoning, application rules, design.

INTELLIGENCE DEFINITION

There are many different definition of intelligence [1--19],

but none of them give the answer acceptable by a scientific

community.

First of all, intelligence is a fuzzy term. In some cases it is

very difficult to draw a line between intelligent and nonintelligent natural and artificial systems. For example,

biological adaptation or any kind of evolution can be

presented as learning intelligent ability or non-intelligent

process. It is difficult to determine when expert system

became an AI system. All intellectual activities are triggered

by the goal. “A system can be intelligent only in relation to a

defined goal...” [11]. All kinds of intellectual activities in the

specific area are based on knowledge, but intelligence is not

knowledge. Knowledge is a “tool” of intelligence. If you

don’t understand a goal, you are not capable to reach it. An

ability to learn is an important intellectual ability that can

improve knowledge. Knowledge reinforces intellectual

activities. There are two components of intelligence: general

intelligence that is inherited at birth, and knowledge-based

intelligence that can be improved by learning. Twin studies

support this approach but the twin result of intelligent level

measurement depends on intelligent definition and the

measurement method that still are problems. Professor Ulric

Neisser (Cornell University) notes [30] that in isolated areas

where the gene pool has been unaffected by migration, the

longer that children attend a school, the higher their I.Q.’s on

average. The knowledge base is a module, organized memory

of an intelligent system and knowledge is just a content of

this base. “Intelligence is an internal property of the system,

not a behavior” [20], but a behavior is the main criterion of

an intelligence level. This level can be determined by a test.

The natural system inherits strong information through

genetic code. They have very strong general intelligence. The

artificial system has relatively weak information power from

the hardware and the software.

Inherited “brain power” of natural intelligence is determined

by power of a neuron net (number of neurons and power of

connections: value of a weight function, a threshold and a

transfer function). A process of knowledge collection creates

an information flow through the neuron net and increases

power of connections (Hebb). As a result “brain power”

increases. In a simple brain model a neuron is a variable with

two values: ON and OFF. The simple rule of knowledge

(if...then) in KB can be presented as a variable with two

values. The more rules the more connections between the

variables the higher intellectual power of a system. In AI

systems that are not based on neuron net technology,

increasing a number of rules in KB increase a number of

virtual connection between the different parameters as well.

The knowledge base is the main source of information and

intellectual power of artificial systems. Inheritance is the

main source of natural intelligence power.

A definition is not a description of a system design. Good

definition presents a term from the user (customer,

supervisor, etc.) point of view and helps to recognize it

among the other terms. It should be as simple as possible.

Now we can try to design an intelligence definition.

influence mental and physical behavior in accordance

General intelligence (inherited or hardware intelligence)

is an organized combination of conscious and unconscious

potentials (cognitive and expressive potentials) in a

sentient system that able to direct and with a system goal.

General intelligence is a capability opposite to ability of the

system. It can be evaluated indirectly through electrical and
chemical brain activities are measured by instrumentation. A

level of fuzziness determines a level of confidence.

Knowledge-based intelligence can be defined as a

knowledge-based general intelligence (or ability) of a

domain-oriented system to act under existing constraints

(limitations) and reach external or internal goals or

decrease the distance between the starting and the goal’s

stages. A goal’s description can be presented in crisp,

fuzzy, or probability and statistics theory languages. This

definition covers not just cognitive power but a power of

sensing system and the actuators. In this case cognitive

power is limited by knowledge and extended by learning.

Knowledge-based intelligence can be evaluated by behavior

tests. General intelligence of AI systems can be evaluated by

reading of design documentation and program source code.

Unfortunately access to this information usually is not

available under secrecy conditions. Both of these definitions

of intelligence agree with existing two-factors, multipleintelligence, and information-processing theories of natural

intelligence [23]. This is the extreme definition. As a working

definition of AI system it is possible to accept: the system

with one or more intellectual abilities (Fig 1) or the

system that emulate one or more intellectual abilities.

Note: a condition statement “if-then” in a hard coded

program is not an element of a knowledge base. The

conventional closed-loop information system (control

system) is not knowledge-based. Only the intelligent system

is based on knowledge. This statement supports fuzzy nature

of intelligence definition.

The time it takes to execute the goal is one of many

important characteristics of a system performance such as

learning ability, duration of the object recognition, etc. and

should not be incorporated into the definition. The statement

“...a goal should be reached for a certain period of time...”

does not make any sense and does not make the definition

better.

In some discussions we can hear that sometimes a high

intelligent system performs some specific job worse then a

lower level of a intelligence system (in human society we

have the same). So what, don’t use the tractor instead of the

hammer. The right choice is a very important characteristic

of human and artificial intellect as well.

CLASSIFICATION OF THE

INTELLIGENCE TASKS AND

ABILITY OF THE AGENTS TO ACHIEVE

THEIR GOALS

The system design is based on set of desirable system tasks

(abilities) and relationships between them. A conventional

software design technology creates the programs for the

specific problem solution. From the programmer point of

view AI is a software design technology to create programs

with intellectual abilities. These programs can be used for

wide area of the problem solutions.

What kinds of intellectual tasks do we have? Who is more

intelligent or smarter: a scientist or a wood-maker (human or

machine), a metal-maker or a wood-maker? In [23] we can

read: “Who’s more intelligent: a Supreme Court Justice or

professional golfer?” Task classification can help to design

system.

Intelligence abilities can be presented as the multilevel

structure [2,32]. But this structure presents a system view

from one side. A multilevel structure of functions

(abilities) (see Fig. 1) with expressive and cognitive thinking

at the upper levels of the structure; learning, problem solving,

and etc at the middle level; and generalization, reasoning,

conceptualization, induction, information collection,

perception, etc. at the lower level of the structure presents the

system from another point of view. Perception can be

presented as a set of the different signal, emotion – as a set of

the different kinds of emotions. Conceptualization itself

consists of two levels: identification of important

characteristics and identification of how the characteristics

are logically linked. Certainly this structure is based on some

level of simplification of the relationship as well as the set

size of abilities. But any way, this structure can help to

determine the set of abilities related to the certain goal, their

relationships, and determine the metric structure to evaluate

the system intelligence levels. It takes longer to exercise the

upper level abilities than the lower level abilities. Different

tasks need different sets of abilities to fulfil these tasks.

“Animal behavior ought to be used as a model to define a

hierarchy of intelligence tasks”[28].

The structure of the intelligent functions was discussed early,

for example, in [20]. In accordance with the definition in this

paper “intelligence is an ability...” but what kind of abilities

are “the information and values the system has stored in its

memory”? The mixture of different levels like reasoning and

problem solving (reasoning is the lower level ability

relatively to problem solving), reward and punishment with

value judgement (reward and punishment is the lower level

ability relatively to value judgement) creates the wrong

structure. Computation power (speed, sophistication of the

algorithm of computation or something else?) and number of

processors, knowledge representation mechanisms and

symbols (symbols of what?), and many others are placed in

one row as dimensions of intelligence.

In [1] and [2] we have a very clear answer to the goal

importance problem. The goal is a result of the intelligent

system actions. “A system can be intelligent only in relation

to a defined goal or environment”[11]. Different tasks,

different areas of activities have different goals. Similar goals

can be combined into one class, which we can call the goal
class. The goal class (similarity) is determined by minimal

set of abilities to fulfil the goal of the task with the same

weight functions of each ability. All agents that exercises the

same minimal set of abilities to carry out the goal with the

same set of weight functions can be combined into one class

which we can call the agent class. The members of the same

agent class can fulfil the goals of the same goal class.

A scientist, a wood-maker, and a metal-maker are trained to

perform different classes of tasks (goal classes) and we

cannot make any comparisons between different agents of

different agent classes. So, it is impossible to compare a

scientist and a handyman, as long as they fulfill different

tasks under different goals. In some cases it is possible to

combine the systems with visible different intelligence levels

into one agent class. For example, agent from the “handyman

class” and agent from the “scientist class” can be combined

into one class if these systems act under similar goals as for

example, surviving, reproduction, repairing something that

does not need any special scientific knowledge, etc.

Performance of these systems and level of their intelligence

can be compared. Multiple-intelligence theory [23] supports

this point of view. Achievement of the same goal by the

different agents usually involves the same set of their abilities

with the same set of weight functions. It is impossible to

compare a car and bookstore even if you use the money scale

to evaluate them. But as soon as you look at them as

investment choices (taxi or shop), you will be able make a

comparison: the same goal (profit) and the same set of

characteristics. The stock market permits the use money scale

to compare almost everything because the same investment

goal and the same parameters of evaluation. Good gamblers

in reality use vector function, but non-sophisticated people

play by price difference.

It is reasonable to suppose that a scientist has better training

in abstract abilities than a handyman. It is reasonable to make

serious decision about differences of the intelligence level of

these systems. Different domain applications are determined

by different sets of abilities. But it is possible that a

handyman (human or machine) has grater level of

intelligence (special abilities) then a scientist (human or

machine). If these handyman’s special extra abilities are not

fit to the his/her/its kinds of activities then they can not be

utilized in the professional activities of a scientist and a

handyman as well. Performance of the different tasks utilizes

the certain limited sets of intelligent abilities. In this case a

very smart metal-maker will not be able to use full his/her/its

available intelligence power and will not be able to

demonstrate the full set of abilities that are not important to

fulfil standard metal-worker task.

In order to make an evaluation of a real “brain” power of the

system, we should assign a reasonable and comparable goal

level. It is important to avoid using the overqualified agent.

By the way, it is a big problem of the job market.

Human intelligence is not a subject-oriented set of abilities.

We are not talking about a genius; we are talking about

ordinary people. I myself don’t understand the nature of

genius. Machine intelligence (for the time being) is a subjectoriented ability. There are different levels (capacities) of

intelligence. Sometimes different levels of performance

(skills) can be presented as different levels of intelligence.

Different levels of performance are determined in many cases

by limitation of one or more elements of the system.

Advanced upper level abilities of the intelligent structure

(generalization, conceptualization, etc.) are not guarantying a

high level of the skills. For example, low capability of the

sonar sensors can prevent a person to be a musician even if

he/she/it has a suitable capability of the rest of the

subsystems. Beethoven was not a deaf man; he lost his ability

to hear. Composer as music designer can “hear” his music

with his inner “sensor”. The famous woman Helen Keller,

author and educator was deaf, blind and mute but she had a

sensitive tactile system and sense of smell. She learns to

“hear” and to speak and she was able to make her great

intellectual power work [14]. A scientist with a high level of

intelligence may have a problem doing a manual job if

he/she/it does not have suitable actuators. A “handyman” is

not a handyman without hands. There are two choices to

design the definition of intelligence: to extend definition and

include sensors and actuators or to add separate explanation

of sensors and actuator importance. As soon as we talk about

intelligence as ”...an ability of a system to act

appropriately...”[1], we include an actuator into this

definition. No sensors – no knowledge, no actuators – no

performance; and it is impossible to evaluate the level of

intelligence.

Globe Institute of Technology has strong positive

experiences to reeducate people of different backgrounds into

very good programmers. Our experience shows that a

medical doctor, a psychologist, an engineer, a teacher, and

people who worked in many other fields, can fulfill tasks of

the high level programmer and they like doing it. There are a

lot of people who are good scientists, medical doctors or have

other professions and at the same time are good writers or

musicians, biologists or good mathematicians, etc. These

examples support the assumption that human intelligence is

determined by a goal achievement activity level but not by

area of application. In other words, agents in many different

domains can be combined in one agent class if they exercise

the same minimal set of abilities at the same levels.

 AGENT WITH REASONING. THE STRUCTURE

DESIGN

Reasoning is the most powerful intellectual function but it is

not easy to emulate it. The main problem is determined by

the nature of reasoning that is based on computation with

words instead of computation with numbers. There are a lot

of different approaches to the knowledge representation in

the agents. The most important languages of knowledge
representation are preposition logic and predicate logic.

Agent models of reasoning based on preposition and

predicate logic are topics of this discussion.

Reasoning, as we know, is the process of drawing conclusion

from facts. There is a lot of research dedicated to the

problems of reasoning and the agent structure design

[7,9,18]. All of them are based on representation of

knowledge as rule-based, semantic net, or frame structure

knowledge base. These knowledge bases (KB) include just

application knowledge (AKB) (domain oriented KB). Rules

of reasoning are applied on AKB in different ways for

different agents. This approach decreases the level of

universality of the agent. Most existing systems with

reasoning are not universal theorem provers (http://wwwformal.stanford.edu/clt/ARS/Entries/acl2). These systems are

based on rules of reasoning and don’t work with application

knowledge. Some of them, like ACL2, are designed as multiKB with (Deductive machinery, Dynamics, Persistence).

However, all these systems are based just on preposition

logic. The most interesting result in the area of reasoning is

the Jess language ( Jess, the Java Expert System Shell

http://herzberg.ca.sandia.gov/jess/demo.html). This language

is based on just one KB-AKB. Information is presented by

predicate logic. Rules of reasoning are incorporated into a

source code.

A possible way to increase the level of universality of the

agent is by creating the double KB agent structure. The first

KB is application knowledge base (AKB); the second one is

rule of reasoning KB-RKB. RKB is universal KB. It can be

used with different AKB. The Double-KB structure of a

system (the programmer Mr. U.Rozenblad) is shown on

Fig.2. Complicated application rules should be decomposed

to simple rules by DeMorgan’s, associative, and other laws.

The idea of a multi-KB in search engines also was described

by Dr. Lotfi Zadeh in “The Prototype-Centered Approach to

Adding Deduction Capability to Search Engines- The

Concept of Protoform” (BISC letter, 21 Dec 2001)

http://www.cs.berkeley.edu/People/Faculty/Homepages/zade

h.html . In this letter: “The deduction database is assumed to

consist of logical database and a computational database,

with the rules of deduction...” Rules of deduction are

Implication Elimination, And–Elimination, And-Introduction,

etc. These rules transfer rules of application in canonical

form. Transformation can be done during of application role

presentation or during a program execution. First way is more

time efficient, second one does not change of application

roles presentation and make them easy recognizable.

Advantages of reasoning rules separation from a program:

1. Simple choice of the set of rules from the prepared list of

rules for each area of application.

 2. Standardization of a program by coding only reading

functions and functions of recognition. The standard program

can be easy designed and testing. The standard program has a

high level

of universality and can be easy adapted to the different areas

of application.

3. Separation of rules of reasoning from a program makes a

program easy readable, better understandable, and as a result

more reliable.

INTUITION

It is not the question: does machine have intuition or doesn’t

have it? If we are machines and we have intuition then a

machine has intuition. The problem is to define the word

intuition to make it worktable. There is a lot of different

definitions [ 3,8,10,12,15,16,18,21,24,25,31,32,34]. From the

practical point of view we need the positive, constructive

approach even if in the beginning we design system just with

the realization of the simple process of the intuition imitation.

The most famous definition of intuition is “the immediate

knowing, or learning of something without the conscious use

of reasoning; instantaneous apprehension” (Webster’s New

universal unabridged dictionary). The difference between

intuition and association (by Webster’s) is: the first is a nonconscious process, the second is a conscious process. This

definition is not productive.

It is impossible to extract knowledge from nothing. If you

never heard about the stock market or brain surgery, you will

never have intuitive decision in these areas. Knowledge

extraction is a conscious process. There are two conditions

under which one idea is able to recall another. “These

conditions may be classified under two general heads, the law

of contiguity (in reality is law of associations), and the law of

associations (in reality is law of reasoning). The first states

the fact that actions, sensations, emotions, and ideas, which

have occurred together, or in close succession, tend to

suggest each other when any one of them is afterward

presented to the mind. The second indicates, or ideas tend to

recall their like from among previous experiences. On their

physical side the principles of association correspond with

the physiological facts of reexcitation of the same nervous

centers” (Webster’s New universal unabridged dictionary).

These two definitions relate to two different processes. One is

associated thinking, second one is intuition.

The memory is a network hierarchy [23]. It is arrangement of

nodes or categories such that concrete ideas are at the bottom

of the hierarchy and are connected to more abstract ideas

above them. The most abstract ideas are at the top. Intuition

is the process of searching a problem solution and ideas

along the hierarchy of a memory. Association is the

process of searching a problem solution and ideas

through direct relationship between them. Analogy is

based on semantic similarity, similarity of memorization time

combination of the objects in the set based on different

criterions, etc. Intuition is a result of free “travel” through the

memory structure. The typical example of associative

learning is a baby learns to associate the smell of its mother
with food (classical conditioning). A student learns that

working hard usually produces good grades (operating

conditioning) [26]. This definition is worktable, reasonable

and non-contradictable. Such presentation of intuition may

not be the best but is very productive for artificial intelligence

system design. In [33] there is description of personality with

intuition as a personality with “focus on implication and

inferences.”

Associative thinking creates the net between different

objects, events, and images. Intuition is not just the search of

the similar solution of the problem but sometimes is

“design” of a solution as a sophisticated assembly of the

several elements. In this case we deal with more complicated

procedures. From the external point of view intuition looks

like associative thinking.

 In opposite, the research and decision searching are

motivated intendment organized processes of a solution of a

problem searching. Importance of intentionality is mentioned

by many philosophers. For Edmund Husserl (German

philosopher) intentionality is “one essential feature of any

consciousness”. For Jean-Paul Sartre (French

philosopher and writer) “intentionality is consciousness”.

Spontaneous brain activities can be triggered by a non-verbal

fuzzy defined problem that is dominated in the memory at

this particular time. In this case, accidental knowledge

activates the algorithm searching for patterns, history,

relationships and etc. to find solution of the problem. The

more data and information that is stored in the memory, the

better the result of the intuition process. The higher

information diversity the more efficient an intuition solution.

There are two kinds of information: genetic and non-genetic.

In artificial systems genetic information is stored in the

hardware and partly in software and contributes to the

artificial intuition.

Intuition is an “automated” high speed process. Cognitive

thinking in most cases is a low speed “manual” process

executed under control of human will.

All knowledge about objects and processes has to be

presented as models designed from the different points of

view (structural models, math models, logical models,

chemical models, electrical and information model, etc). For

example, a human body can be presented in the different

ways as a structured model, a chemical model, an

information model, a mechanical model, etc. Such ways of

knowledge presentation make it possible to easily identify

common features in different areas. The structured

organization of the knowledge in the memory is a very

important condition of effective functioning of the artificial

intuition. In our case, (artificial system). we don’t have

problem of the natural brain in attaching meaning to the

symbolic representation [21]. Existence of the memory

makes reasonable the materialist point of view and cognitivist

point of view as well [21]. Anyway this problem is not a

subject of our discussion.

In the reconstruction of new knowledge when any past event

or experience is recalled, the act of recollection tends to bring

again into use other events and experiences that have become

related to this event in one or more of certain specific ways

this association. Associative memory refers to the ability to

recall complete situations from partial information These

systems correlate input data with information stored in

memory Information can be recalled from even incomplete

input. Associative memory can detect similarities between

new input and stored patterns [10, 12, and 21]. So intuition

and association should work together. Realization of the

associative memory can be done as the Hopfield Neuron

Network [21].

Spontaneous brain activities can be triggered by spontaneous

interest of the system to the problem. For example the

problem of dangerous environment for the system existence.

It can be the cause of spontaneous problem formulation.

Spontaneous undependable problem formulation is possible

just in case of the availability of the powerful sensor system.

This system collects information about simple, separately

non-dangerous events, puts it together independently from a

human will, looking for patterns and creates a sense of

danger. The process and information are presented in fuzzy

description.

Let us look at the simple scenario. At nighttime you left a

party with your friends and were going home. You were

thinking about the good time you had. Suddenly you step-into

a dark street as a part of your way home (level of darkness

may be different-fuzzy descripted). Nothing is wrong around

but your body becomes alerted even if you try to calm

yourself through reasoning. Intuition vs. reasoning!

Intuition can win because reasoning is based on the same

knowledge! Reasoning can just add some new information

and knowledge. As a result, correction of the sense and

behavior can be obtained.

When we meet a stranger, we receive a complex of

information about his/her appearance, body language, and

way of talk, etc. Our brain compares this information with

the fuzzy or statistical models of a “good” or “bad” object

appearance, behavior, etc. and creates our “fuzzy” impression

model about this object. “Good” or “bad” object models are

based on our previous experience. This situation was

emulated on the computer (the programmer Ms. N.

Elisseeva). The system was able to generate intuitive

impression at the meeting with a stranger (Fig. 3).

Unintentional brain activity can include the testing

procedures. One day I sent an e-mail but forget to attach my

file I promised to my friend. I was sure that I did not make a

mistake. In the middle of the night, I suddenly woke up and
realized that I did not attach the file. My brain was testing my

activities stored in the short memory against the goal

procedure and sent me the error massage. It’s remind

automatic virus testing software when we rebut the computer

without special activation. Certainly, it is just analogy. This

ability the control a human activities is a very useful part of

Artificial Intelligence. The “Testing” module can do this test.

The described approach can be illustrated by another

example. Suppose we have the AI system, which has

extensive working experience in the different areas of

knowledge and powerful learning ability from the

experienced external teacher. The knowledge is represented

as the models: linguistic, math, logical, structured, etc, above.

Suppose the system has knowledge about a damper, which is

presented as a linguistic model (damper, controller of

acceleration, brakes, and etc) and as a math equation of a

damper:

y=(1+e –t/T )

or the transfer function

W(p) = k/(1+Tp),

as a physical object (hydrodamper, pneumatic damper,

capacitor, inductance, robber damper, spring, mechanical

brakes, electrical brakes, flywheels etc), and so on. All these

models create the hierarchical structure in the knowledge

base. The more abstract the description, the higher location

levels. The linguistic description belongs to the higher level.

The physical description belongs to the lower level. Suppose

we have a control system and would like to reduce the

acceleration of the moving parts. The AI system has

information (through the sensors) about the problem and

starts looking at a solution of the problem without our

interference. The search procedure is shown in Fig.4. Each

level represents a new level of goals. Each new goal

motivates a next search step.

Intuition can be activated in the slipping stage when the brain

is working without participation of the human will. In an

interview with The New York Times (Nov. 14, 2000) Dr.

Terrence J. Sejnowski (a neuroscientist at the Salk Institute in

San Diego) said: “There has always been a close connection

between sleep and creativity, which may be a byproduct of

the way that nature chose to consolidate memories”.

CONCLUSION:

 1. Intelligence consists of two parts: inherited and

developed.

 2. Intelligence abilities can be presented as the functional

multilevel structure. Similar goals of the agents can be

combined into the goal class. The goal class is

determined by minimal set of abilities to fulfil this goal

of the task and one set of weight function.

 3. All for each alternative – class member agents that

exercises the same minimal set of abilities and common set

of weight functions to carry out the goal can be

combined into the agent class. The members of the same

agent class can fulfil the goals of the same goal class.

4. The structure of intelligence system should be designed as a

two-knowledge base system. One is an application knowledge

base, another one is a reasoning knowledge base.

 5. Intuition is the process of searching a problem solution and

ideas along the hierarchy of a memory. Association is the

process of searching a problem solution and ideas through

direct relationship between them.

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Mishlove, 1998.
Fig. 1.Structure of the most important intelligent abilities

Intellectual

abilities

Expressive Cognitive (Logical)

Imagination Emotions Learning Creativity (Problem solving)

Generalization Conceptualization Induction

Perception

Judgement

Reasoning

Conceiving

Recognition

Identification of

important

characteristics

Identification of

how the

characteristics are

logically linked

Sensation

Intuition

Associative

thinking

Discrimination
 RKB AKB

 System

 Fig. 2 The double-KB system structure

 Amygdala converts words into real feeling [23].

 Occure: 80% 20% 20% 80%

Level of good feeling from a circle: 0.8 – 0.2 = 0. 6

Level of good feeling from a circle: 0.8 – 0.2 = 0. 6

“Good” and “bad” can be described as fuzzy variables.

Fig. 3 The memory structure.

Reasoning

Knowledge base

(Rules of reasoning)

Database (Factscurrent situation)

Inference engine

User interface Developer interface

Translator

Application

Knowledge base

(Application rules)

Feeling

Good feeling Bad feeling

Good object Bad object

Circle Square Circle Square
 The goal description

 Math models

Fig. 4. Acceleration control system design (search through the goal hierarchy).

Fig. 5. The Artificial Intuition System Structure.

Acceleration

control

Damper

Electrical

equipment

Hydraulic

equipment

Physical

objects

(models)

Brake

Math

models

Physical

objects

(models)

Acceleration changing

Fast ® Slow

Information

structure

creation

Knowledge

base

Fuzzy

problem

description

Knowledge

extraction

Solution

synthesis

Problem

description

defuzzines

Trigger of

action

Sensor

system

Solution

Model

design

Testing