Download INTELLIGENT SYSTEMS

Presented by:
ENNAHIBI Med
KASMI Ilyas
OUBAALLA Wafaa
Supervised by:
Mr ABELLAOUI Mohammed
Plan:
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Introduction.
Intelligence in general.
What is Artificial Intelligence?
Historic of Artificial Intellignce: Turing test.
Experts systems.
 What is an Expert system.
 Requirement of Expert System.
 Uses of experts systems.
 Architecture of experts systems.
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Automatic Learning and Neuro-fuzzy.
Intelligent system.
Appliances of Artificial Intelligence.
Conclusion.
Introduction
Intelligence in general.
 Adaptability to a new environment or to changes in the current environment
 Capacity for knowledge and the ability to acquire it
 Capacity for reason and abstract thought
 Ability to comprehend relationships
 Ability to evaluate and judge
 Capacity for original and productive thought
What is Artificial Intelligence?
Artificial intelligence allows computers to learn from
experience, recognize patterns in large amounts of complex data and
make complex decisions based on human knowledge and reasoning
skills.
Turing Test
 The Turing test is a proposal for a test of a machine's
ability to demonstrate intelligence.
What is an Expert System?
An expert system is
software that attempts to
reproduce the performance of
one or more human experts,
most commonly in a specific
problem domain, and is a
traditional application and/or
subfield of artificial intelligence.
Requirement of Expert System:
 The creation of a so-called "knowledgebase" which
uses some knowledge representation formalism to
capture the Subject Matter Experts (SME) knowledge.
 A process of gathering that knowledge from the SME
and codifying it according to the formalism, which is
called knowledge engineering.
Uses of Expert System:
As a first application of computing and artificial
intelligence, the topic of expert systems has many points
of contact with general systems theory, operations
research, business process reengineering and various
topics in applied mathematics and management science.
Architecture of Expert Systems:
 The sequence of steps taken to reach a conclusion is
dynamically synthesized with each new case. It is not
explicitly programmed when the system is built.
 Expert systems can process multiple values for any
problem parameter. This permits more than one line of
reasoning to be pursued and the results of incomplete (not
fully determined) reasoning to be presented.
 Problem solving is accomplished by applying specific
knowledge rather than specific technique.
Automatic Learning and Neuro-Fuzzy:
 In the field of artificial intelligence, neuro-fuzzy refers to
combinations of artificial neural networks and fuzzy logic.
Neuro-fuzzy hybridization results in a hybrid intelligent
system that synergizes these two techniques by combining
the human-like reasoning style of fuzzy systems with the
learning and connectionist structure of neural networks.
Requirement of Expert System:
 The creation of a so-called "knowledgebase" which uses
some knowledge representation formalism to capture the
Subject Matter Experts (SME) knowledge.
 A process of gathering that knowledge from the SME and
codifying it according to the formalism, which is called
knowledge engineering.
Uses of Expert System:
As a first application of computing and artificial
intelligence, the topic of expert systems has many
points of contact with general systems theory,
operations research, business process reengineering
and various topics in applied mathematics and
management science.
Architecture of Expert Systems:
 The sequence of steps taken to reach a conclusion is
dynamically synthesized with each new case. It is not
explicitly programmed when the system is built.
 Expert systems can process multiple values for any
problem parameter. This permits more than one line of
reasoning to be pursued and the results of incomplete (not
fully determined) reasoning to be presented.
 Problem solving is accomplished by applying specific
knowledge rather than specific technique.
Automatic Learning and Neuro-Fuzzy:
 In the field of artificial intelligence, neuro-fuzzy refers
to combinations of artificial neural networks and
fuzzy logic. Neuro-fuzzy hybridization results in a
hybrid intelligent system that synergizes these two
techniques by combining the human-like reasoning
style of fuzzy systems with the learning and
connectionist structure of neural networks.
Intelligent Systems :
“In order to make great games, great tools and a great work
environment are essential.”
INTELLIGENT SYSTEMS began by supporting
Nintendo game development from the very
beginning. At first, development tools were required
in order to develop game software.
Intelligent Systems II:
“INTELLIGENT SYSTEMS creates it own unique game development
environment.”
INTELLIGENT SYSTEMS grew not
only as a game developer, but as a game
environment and development tool
developer as well.
Appliances of AI: Finance
Banks use artificial intelligence systems to organize
operations, invest in stocks, and manage properties. In August
2001, robots beat humans in a simulated financial trading
competition.
Financial institutions have long used artificial neural
network systems to detect charges or claims outside of the norm,
flagging these for human investigation.
Appliances of AI: Medicine
A medical clinic can use artificial intelligence systems
to organize bed schedules, make a staff rotation, and provide
medical information.
They may also be used for medical diagnosis, Artificial
neural networks are used for medical diagnosis (such as in
Concept Processing technology in EMR software),
functioning as machine differential diagnosis.
Appliances of AI: Heavy industry
Robots have become common in many industries.
They are often given jobs that are considered dangerous to
humans. Robots have proven effective in jobs that are very
repetitive which may lead to mistakes or accidents due to a
lapse in concentration and other jobs which humans may
find degrading. General Motors uses around 16,000 robots
for tasks such as painting, welding, and assembly. Japan is
the leader in using and producing robots in the world. In
1995, 700,000 robots were in use worldwide; over 500,000
of which were from Japan.
Appliances of AI: Transportation
Fuzzy logic controllers have been developed for
automatic gearboxes in automobiles (the 2006 Audi TT, VW
Toureg and VW Caravell feature the DSP transmission which
utilizes Fuzzy logic, a number of Škoda variants (Škoda Fabia)
also currently include a Fuzzy Logic based controller).
Appliances of AI: Toys and games
The 1990s saw some of the first attempts to massproduce domestically aimed types of basic Artificial
Intelligence for education, or leisure. This prospered
greatly with the Digital Revolution, and helped introduce
people, especially children, to a life of dealing with
various types of AI, specifically in the form of
Tamagotchis and Giga Pets, the Internet (example: basic
search engine interfaces are one simple form), and the first
widely released robot, Furby. A mere year later an
improved type of domestic robot was released in the form
of Aibo, a robotic dog with intelligent features and
autonomy. AI has also been applied to video games.
Appliances of AI: Music
The evolution of music has always been affected by
technology. With AI, scientists are trying to make the
computer emulate the activities of the skillful musician.
Composition, performance, music theory, sound processing
are some of the major areas on which research in Music
and Artificial Intelligence are focusing on.
Conclusion
 Artificial Intelligence is a field that requires
immagination, it has a succeful future in industry,
office also domestic use.
 Thus, video games, robotic, cybertic and
nanotechnologie all of this exceptionnal phenomena
are based on Artificial Intelligence...
 So be carefull because it is critical field, may be one
day, it will turn on humain life.