component of expert system in ai

1. Components of Expert
System
By
Md. Fazle Rabbi
16CSE057

2. 4.2
What is an Expert System?
Why Expert System?
Characteristics of Expert System
Components of Expert System
Advantages of Expert System
Limitations of Expert System
Outlines

3. 4.3
• An expert system is a computer program
• Designed to solve complex problems and to
provide decision-making ability like a human
expert.
What is an Expert System?

4. 4.4
What is an Expert System?

5. 4.5
Examples of the Expert System:
CaDeT:
The CaDet expert system is a diagnostic support system that can detect cancer
at early stages.
DENDRAL:
To detect unknown organic molecules with the help of their mass spectra and
knowledge base of chemistry.
PXDES:
It is an expert system that is used to determine the type and level of lung
cancer.

6. 4.6
Why Expert System?

7. 4.7
• High Performance:
The ES provides high performance for solving any type of
complex problem.
• Understandable:
It responds in a way that can be easily understandable by
the user.
• Reliable:
It is much reliable for generating an efficient and accurate
output.
• Highly responsive:
ES provides the result for any complex query within a very
short period of time.
Characteristics of Expert System

8. 4.8
Components of Expert System

9. 4.9
User Interface

10. 4.10
• The expert system interacts with the user.
• Takes queries as an input in a readable format,
and passes it to the inference engine.
• It is an interface that helps a non-expert user to
communicate with the expert system to find a
solution.
User Interface

11. 4.11
Inference Engine(Rules of Engine)

12. 4.12
Inference Engine(Rules of Engine)
• It is the brain of the expert system.
• It is the main processing unit of the system.
• It applies inference rules to the knowledge
base to derive a conclusion.
• It helps in deriving an error-free solution of
queries asked by the user.

13. 4.13
Inference Engine(Rules of Engine)
There are two types of inference engine:
• Deterministic Inference engine:
The conclusions drawn from this type of inference
engine are assumed to be true. It is based on facts
and rules.
• Probabilistic Inference engine:
This type of inference engine contains uncertainty
in conclusions, and based on the probability.

14. 4.14
Inference Engine(Rules of Engine)
Inference engine uses the below modes to derive
the solutions:
• Forward Chaining: It starts from the known facts
and rules, and applies the inference rules to add
their conclusion to the known facts.
• Backward Chaining: It is a backward reasoning
method that starts from the goal and works
backward to prove the known facts.

15. 4.15
Knowledge Base

16. 4.16
• A knowledge base is an organized collection of facts
about the system’s domain.
• Facts for a knowledge base must be acquired from human
experts through interviews and observations.
• This knowledge is then usually represented in the form of
“if-then” rules (production rules): “If some condition is
true, then the following inference can be made (or some
action taken).”
• A probability factor is often attached to the conclusion of
each production rule and to the ultimate recommendation,
because the conclusion is not a certainty.
Knowledge Base

17. 4.17
• Factual Knowledge
It is the information widely accepted by the
Knowledge Engineers and scholars in the task domain.
• Heuristic Knowledge
It is about practice, accurate judgement, one’s
ability of evaluation, and guessing.
Components of Knowledge Base

18. 4.18
• The knowledge base is formed by readings from various
experts, scholars,and the Knowledge Engineers. The
knowledge engineer is a person with the qualities of
empathy, quick learning, and case analyzing skills.
• He acquires information from subject expert by recording,
interviewing, and observing him at work, etc.
• He then categorizes and organizes the information in a
meaningful way, in the form of IF-THEN-ELSE rules, to be
used by interference machine. The knowledge engineer
also monitors the development of the ES.
Knowledge Acquisition

19. 4.19
Advantages of Expert System
• These systems are highly reproducible.
• They can be used for risky places where the
human presence is not safe.
• Error possibilities are less if the KB contains
correct knowledge.
• The performance of these systems remains
steady as it is not affected by emotions,
tension, or fatigue.
• They provide a very high speed to respond to a
particular query.

20. 4.20
Limitations of Expert System
• The response of the expert system may get wrong if
the knowledge base contains the wrong information.
• Like a human being, it cannot produce a creative
output for different scenarios.
• Its maintenance and development costs are very high.
• Knowledge acquisition for designing is much difficult.
• For each domain, we require a specific ES, which is
one of the big limitations.
• It cannot learn from itself and hence requires manual
updates.

21. 4.21
Thank you