Introduction to Artificial

Intelligence
CT098-3-M Ver 1.0

Expert Systems
 Topic & Structure of the Lesson

• Expert System definition

• Categories of Expert System
• Classical Expert System
• Expert System Life Cycle
• Expert System Component
• People involve in Expert Systems

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 Recap AI

1. Artificial Intelligence suggests the Intelligence of a

machine which is artificial.
2. The intelligence posses by human is known as human
intelligence, like in the same way the intelligence
demonstrated by a machine is known as Artificial
Intelligence.
3. In computer science. Artificial intelligence(AI),
sometimes called machine intelligence.
4. The research field of Artificial Intelligence was born at a
workshop at Dartmouth College in 1956.

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 Applications Of Artificial Intelligence In Real
World

• The chatbots like SIRI, CORTANA which have gained so much of

popularity in nowadays.
• Other examples like EVA (Electronic Virtual Assistant), an AI-based
chatbot developed by HDFC banks’s AI research department which
can collect knowledge from thousands of sources and provide
simple answers in less than 0.4 seconds.
• There are so many examples of AI applications you will find in
different field of our society.

• Moving on with this Expert System

In Artificial Intelligence

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 What is Expert System?

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 Expert System definition

• Expert systems were the first major economically

successful product resulting from the study of Artificial
Intelligence

• Expert Systems are designed to solve real problems in a

particular domain that normally would require a human
expert.

• Developing an ES involves extracting relevant

knowledge from human experts in the area of problem,
called domain experts

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 Purpose of Expert System

• The main purpose of an expert system is

to acquire knowledge of human experts
and to replicate that knowledge and skills
of human expert in a particular area.
• Then the system will use that knowledge
and skills to solve complex problems of
that particular area without human experts
participation.

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 Categories of Expert System

Advice
Classification
Diagnosis
Planning.

• Refer to notes categories of expert system

and tutorial question

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 Classical ES

• Refer to tutorial notes

• https://www.youtube.com/watch?v=0zf5E
GX3Ons
• Medical expert system

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 Characteristics of Expert
Systems
• High performance
• Understandable
• Reliable
• Highly responsive

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 Expert System Life Cycle
Semantic network,
Production rule – factor
table
Frame

Knowledge engineer –
domain expert / human
expert

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 Knowledge acquisition:

The process of getting knowledge from

experts by interviewing or by observing
human experts, reading specific books, etc.

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 Knowledge Representation

Knowledge representation is the method of selecting the

most appropriate structures to represent the knowledge. It
is the method of organizing and formalizing knowledge in
the knowledge base. It is done in the form of IF-THEN-
ELSE rules.
Types
1. Production Rule – Factor table
2. Semantic network
3. Frame

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 System validation

Testing the knowledge of ES is correct and

complete.

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 People involve ES

Domain expert – the individual or group whose expertise

and knowledge is captured for use in an ES

Knowledge engineer – person in charge of collecting the

knowledge and representing into 1 of the knowledge
representation type

Programmer – person who builds the system

User – using the system

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 What is Knowledge

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 Expert System Component

• Anatomy of an expert system

Expert System Shell

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 Inference Engine

• In case of knowledge-based ES, the Inference Engine

acquires and manipulates the knowledge from the
knowledge base to come at a particular solution.

• In case of rule based ES,

 It applies rules repeatedly to the facts, which are
obtained from earlier rule application.
 It addition of new knowledge into the knowledge base if
required.
 It resolves rules conflict when multiple rules are
applicable to a particular case.

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 Inference Engine uses the
following strategies
• Forward Chaining
Inference Engine gives the outcome by following the chain
of conditions and derivations.
Whatever the knowledge is feeded in the system it goes
through all those knowledges and facts and sorts them
before concluding a solution.
By forward chaining method, expert system tries to
answers, “What can happen next?”
Application of forward chaining: House price prediction,
stock prediction, prediction of share market etc.

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 Backward Chaining

When something has happened in a particular

domain, the Inference Engine tries to find out
which condition could have happened in the past
for this result.
By backward chaining method, expert system tries
to answer, “Why this happened?”.
By backward chaining method inference engine
tries to find out cause or reason.
• For example: diagnosis of blood cancer in
humans.
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 Advantages of Expert System

• Hold huge amounts of information

• Minimize employee training costs
• Centralize the decision making process
• Make things more efficient by reducing the time needed to solve
problems
• Combine various human expert intelligences
• Reduce the number of human errors
• Provide strategic and comparative advantages that may create
problems for competitors
• Look over transactions that human experts may not think of
• Provide answers for decisions, processes and tasks that are
repetitive

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 Disadvantages of expert
system:
• Lack of creative responses that human experts are
capable of
• Not capable of explaining the logic and reasoning behind
a decision
• It is not easy to automate complex processes
• There is no flexibility and ability to adapt to changing
environments
• Not able to recognize when there is no answer
• No common sense used in making decisions

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 Limitations

• It fails to make creative responses as it is a machine.

• If the data which was feeded in the knowledge base is
not accurate or correct it will give wrong predictions and
wrong results.
• Maintenance cost of expert system is high.
• When different problems comes human expert can give
different different solutions and creative responses but
expert system fails to give creative responses

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 Philosophical, practical, legal and
ethical issues
Are expert systems really AI?

• ability to learn - NO
• Creativity - No
• reasoning - Yes
• use of language - yes
• vision - no
• problem solving - yes
• adapting to new situations - no

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 Summary

1. The purpose of an expert system is to draw reasoned

conclusions from a body of knowledge in a limited
domain
2. Expert systems have advantages over human experts -
they are permanent, cost effective, give consistent
advice and are portable;
3. Expert systems have some disadvantage compared to
human experts - they only cover a limited domain, lack
common sense, cannot retain new knowledge, are
inflexible, and take time and expertise to develop;

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 Summary continues

4. An expert system consists of a user interface, a

knowledge base and an inference engine

5. An expert system shell is an expert system with an

empty knowledge base;

6. Expert system shells are used for constructing expert

systems;

7. Expert systems have been used in a huge and ever-

increasing variety of different knowledge domains.

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 Recap

1. What is the purpose of the User Interface in an

Expert System?
2. What is the purpose of the Knowledge Base in
an Expert System?
3. What is the purpose of the Inference Engine in
an Expert System?
4. Distinguish clearly between an expert system
and an expert system shell.

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 Who is to blame

Either on your own, or with another student,

brainstorm a list of people who might be
responsible when an expert system gives
advice which leads to loss of life or damage
to property.

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 Who is to blame?

The following people could be considered responsible in some way:

• the domain expert who provided the knowledge that was used to
create the knowledge base;
• the knowledge engineer who developed the knowledge base from
the domain expert’s input;
• any other programmers involved in developing or testing the user
interface or inference engine of the expert system;
• the software company which sold the expert system to the user;
• the user who accepted the advice given by the expert system.

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 Main areas of application

• Interpretation – drawing high level conclusions based on

data.
• Prediction – projecting probable outcomes.
• Diagnosis – determining the cause of malfunctions,
disease, etc.
• Design – finding best configuration based on criteria.
• Planning – proposing a series of actions to achieve a
goal.
• Monitoring – comparing observed behaviour to the
expected behaviour.
• Debugging and Repair – prescribing and implementing
remedies.
•
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 continues

• Debugging and Repair – prescribing and

implementing remedies.
• Instruction – assisting students in learning.
• Control – governing the behaviour of a
system.

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 Knowledge Representation

1. Production Rule – factor table – refer to

tutorial
2. Frame - tutorial
3. Semantic network – refer to tutorial

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 Conclusion

• https://www.youtube.com/watch?v=IAcscn
F0n3U

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 Question and Answer Session

Q&A

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