An expert system is a computer program that contains knowledge from human experts to solve complex problems in a specific domain. It has four main components: a knowledge base of facts and rules, an inference engine that applies rules to facts to derive new facts, a user interface, and an explanation facility. Expert systems were developed in the 1970s to apply human expertise to problems. They have limitations but can also explain their reasoning, draw complex conclusions, and provide portable knowledge to help humans. Common applications of expert systems include medical diagnosis, materials identification, and credit approval.

1. EXPERT SYSTEMS
BY
Tilak Poudel

2. Overview
•What is an Expert System?
•History
•Components of Expert System
•Who is involved?
•Development of Expert System

3. Overview of Expert Systems
 Can…
 Explain their reasoning or suggested
decisions
 Display intelligent behavior
 Draw conclusions from complex
relationships
 Provide portable knowledge
 Expert system shell
 A collection of software packages and
tools used to develop expert systems

4. Limitations of Expert Systems
 Not widely used or tested
 Limited to relatively narrow problems
 Cannot readily deal with “mixed”
knowledge
 Possibility of error
 Cannot refine own knowledge base
 Difficult to maintain
 May have high development costs
 Raise legal and ethical concerns

5. WHAT IS AN EXPERT SYSTEM?
 An expert system is a
computer program that
contains some of the subject-
specific knowledge of one or
more human experts.

6. History of Expert Systems

7.  Early 70s
 Goal of AI scientists  develop
computer programs that could in
some sense think .
 In 60s general purpose programs
were developed for solving the
classes of problems but this strategy
produced no breakthroughs.
 In 1970 it was realized that The
problem-solving power of program
comes from the knowledge it
possesses.

8. To make a program
intelligent, provide it
with lots of high-quality,
specific knowledge
about some problem
area.

9. Building Blocks of Expert
System

10.  Knowledge base (facts)
 Production Rules ("if.., then..")
 Inference Engine (controls how "if..,
then.." rules are applied towards
facts)
 User Interface

11. Knowledge Base
 The component of an expert system
that contains the system’s
knowledge.
 Expert systems are also known as
Knowledge-based systems.

12. Knowledge Representation
 Knowledge is represented in a
computer in the form of rules
( Production rule).
 Consists of an IF part and THEN part.
 IF part lists a set of conditions in
some logical combination.
 If the IF part of the rule is satisfied;
consequently, the THEN part can be
concluded.

13. Knowledge Representation
 If flammable liquid was spilled then
call the fire department.
 If the material is acid and smells like
vinegar then the spill material is
acetic acid.

14. Chaining of IF-THEN rules to
form a line of reasoning
Forward chaining (facts driven)
Backward chaining (goal driven)

15. Inference Engine
 An inference engine tries to derive
answers from a knowledge base.
 It is the brain of the expert systems
that provides a methodology for
reasoning about the information in
the knowledge base, and for
formulating conclusions.

16. User Interface
It enables the user to
communicate with an expert
system.

17. Other features
Reasoning with uncertainty
Explanation of the line of
reasoning
Fuzzy Logic

18. Who is involved?
?

20. Knowledge Engineer
 A knowledge engineer is a computer
scientist who knows how to design
and implement programs that
incorporate artificial intelligence
techniques.

21. Domain Expert
 A domain expert is an individual who
has significant expertise in the
domain of the expert system being
developed.

22. Knowledge Engineering
 The art of designing and building the
expert systems is known as
KNOWLEDGE ENGINEERING
knowledge engineers are its
practitioners.
 Knowledge engineering relies heavily
on the study of human experts in
order to develop intelligent & skilled
programs.

23. Developing Expert Systems
 Determining the characteristics of the
problem.
 Knowledge engineer and domain
expert work together closely to
describe the problem.

24.  The engineer then translates the knowledge
into a computer-usable language, and
designs an inference engine, a reasoning
structure, that uses the knowledge
appropriately.
 He also determines how to integrate the use
of uncertain knowledge in the reasoning
process, and what kinds of explanation
would be useful to the end user.

25.  When the expert system is
implemented, it may be:
 The inference engine is not just right
 Form of representation of knowledge is
awkward
 An expert system is judged to be
entirely successful when it operates
on the level of a human expert.

26. Questions
Indentifi-
cation
Indentifi-
cation
Conceptu-
alization
Conceptu-
alization
Formali-
zation
Formali-
zation
Rule
Formalization
Rule
Formalization ValidationValidation
Knowledge
Concepts
Structure
Rules
Conclusion
Representation
Refinements
Re-designment
Stages for designing knowledge base
Stages for Designing KB
Concepts
identify what the
problem is , how
to define it , can
we divide it into
some sub
problems
define key concept of the
knowledge ,for example :
type of data structure ,
conditions that have
known, the goal state,
assumption and control
strategy.
use knowledge
representation
method to
represent the
knowledge.
change the
knowledge to
programming
language that can be
identified by the
computer.
check the
correctness of
rules or
knowledge

27. Human Expertise vs Artificial
Expertise
1. Perishable
2. Difficult to transfer
3. Difficult to document
4. Unpredictable
5. Expensive
1. Permanent
2. Easy to transfer
3. Easy to document
4. Consistent
5. Affordable

28. Some Prominent Expert
Systems
 Dendral
 Dipmeter Advisor
 Mycin
 R1/Xcon

29. Determining requirements
Identifying experts
Construct expert system components
Implementing results
Maintaining and reviewing system
Expert Systems Development
Domain
• The area of knowledge
addressed by the
expert system.

30. Applications of Expert Systems
and Artificial Intelligence
 Credit granting
 Information management and retrieval
 AI and expert systems embedded in products
 Plant layout
 Hospitals and medical facilities
 Help desks and assistance
 Employee performance evaluation
 Loan analysis
 Virus detection
 Repair and maintenance
 Shipping
 Marketing
 Warehouse optimization

31. THANK YOU
THE END