This document provides an overview of expert systems, including their history, types, components, applications, and development process. Some key points: - Early expert systems included DENDRAL for chemical analysis and MYCIN for medical diagnosis. Expert systems were pioneered in the 1970s and 1980s. - Expert systems can advise on complex problems like a human expert through rule-based or frame-based knowledge representation. - The main components are the knowledge base containing domain expertise, an interface engine that uses rules to solve problems, and a user interface. - Expert systems have various applications in fields like design, medicine, monitoring, and finance. However, they also have limitations like knowledge acquisition difficulties

1. Present by: Abdul Ahad Abro
Expert System ES
Prof. Dr. Aybars UĞUR
.

2. Expert System
History of Expert Systems
Early Expert Systems
Expert Systems Types
Characteristics of Expert Systems
Capabilities of Expert Systems
Components of Expert Systems
Knowledge Base
Interface Engine
User Interface
Expert Systems Limitations
Applications of Expert System
Development of Expert Systems: General Steps
Benefits of Expert Systems
Disadvantage
.
Expert System

3. Expert systems (ES) are one of the prominent
research domains of AI. It is introduced by the
researchers at Stanford University, Computer
Science Department.
.
ES

4. What are Expert Systems?
The expert systems are the computer
applications developed to solve complex
problems in a particular domain, at the
level of extra-ordinary human intelligence
and expertise.
.

5. What are Expert Systems? (2)
An expert system is a computer system
that emulates, or acts in all respects, with
the decision-making capabilities of a
human expert.
.

6. History of Expert Systems
Expert systems were introduced by the Stanford Heuristic Programming Project led by
Feigenbaum, who is sometimes referred to as the "father of expert systems". The Stanford
researchers tried to identify domains where expertise was highly valued and complex, such as
diagnosing infectious diseases (Mycin) and identifying unknown organic molecules (Dendral).
In addition to Feigenbaum key early contributors were Edward Shortliffe, Bruce Buchanan, and
Randall Davis. Expert systems were among the first truly successful forms of AI software.
.

7. History of Expert Systems (2)
In the 1990s and beyond the term "expert system" and the idea of a standalone AI system
mostly dropped from the IT lexicon. There are two interpretations of this. One is that "expert
systems failed": the IT world moved on because expert systems didn't deliver on their over
hyped promise. The fall of expert systems was so spectacular that even AI legend Rishi Sharma
admitted to cheating in his college project regarding expert systems, because he didn't consider
the project worthwhile
.

8. History of Expert Systems (3)
The other is the mirror opposite, that expert systems were simply victims of their success. As IT
professionals grasped concepts such as rule engines such tools migrated from standalone tools
for the development of special purpose "expert" systems to one more tool that an IT
professional has at their disposal. Many of the leading major business application suite vendors
such as SAP, Siebel, and Oracle integrated expert system capabilities into their suite of products
as a way of specifying business logic.
.

9. Early Expert Systems
DENDRAL – used in chemical mass spectroscopy to identify chemical constituents
MYCIN – medical diagnosis of illness
DIPMETER – geological data analysis for oil
PROSPECTOR – geological data analysis for minerals
XCON/R1 – configuring computer systems
.

10. Expert Systems Types
Expert Systems Versus Knowledge-based Systems
Rule-based Expert Systems
Frame-based Systems
Hybrid Systems
Model-based Systems
Ready-made (Off-the-Shelf) Systems
Real-time Expert Systems
.

11. Preferred Languages in Developing ES
LISP - list processing language (MIT). John McCarthy, 1950s.
In the U.S., LISP was the language of choice.
Powerful in its symbolic processing capability, but difficult to master.
PROLOG - logical programming language. Marseille, France, 1970.
Researchers in the U.K. and Japan adopted PROLOG for developing intelligent programs.
It was also the language chosen in Japan for the Fifth Generation effort. Based in a formal well-
understood logic, PROLOG offers a language to develop exact deductive programs.
Like LISP, PROLOG required a disciplined student to master it, thus limiting the number of
competent programmers.
.

12. Structure of a Rule-Based Expert System
.
.

13. Characteristics of Expert Systems
High performance
Understandable
Reliable
Highly responsive
.

14. Capabilities of Expert Systems
The expert systems are capable of − They are incapable of -
Advising Substituting human decision makers
Instructing and assisting human in decision making Possessing human capabilities
Demonstrating Producing accurate output for inadequate knowledge base
Deriving a solution Refining their own knowledge
Diagnosing
Explaining
Interpreting input
Predicting results
Justifying the conclusion
Suggesting alternative options to a problem
.

15. Components of Expert Systems
The components of ES include −
Knowledge Base
Interface Engine
User Interface
.

16. Knowledge Base
It contains domain-specific and high-quality knowledge. Knowledge is required to exhibit intelligence. The success of any
ES majorly depends upon the collection of highly accurate and precise knowledge.
User Interface
User interface provides interaction between user of the ES and the ES itself. It is generally Natural Language Processing
so as to be used by the user who is well-versed in the task domain. The user of the ES need not be necessarily an expert
in Artificial Intelligence.
Interface Engine
Use of efficient procedures and rules by the Interface Engine is essential in deducting a correct, flawless solution.In case
of knowledge-based ES, the Interface Engine acquires and manipulates the knowledge from the knowledge base to
arrive at a particular solution.
.

17. Expert Systems Limitations
No technology can offer easy and complete solution. Large systems are costly, require significant
development time, and computer resources. ESs have their limitations which include −
Limitations of the technology
Difficult knowledge acquisition
ES are difficult to maintain
High development costs
.

18. Applications of Expert System
.
.
Application Description
Design Domain Camera lens design, automobile design.
Medical Domain Diagnosis Systems to deduce cause of disease from observed data, conduction medical operations on humans.
Monitoring Systems Comparing data continuously with observed system or with prescribed behavior such as leakage monitoring in
long petroleum pipeline.
Process Control Systems Controlling a physical process based on monitoring.
Knowledge Domain Finding out faults in vehicles, computers.
Finance/Commerce Detection of possible fraud, suspicious transactions, stock market trading, Airline scheduling, cargo scheduling.

19. Development of Expert Systems: General Steps
The process of ES development is iterative. Steps in developing the ES include
Identify Problem Domain
The problem must be suitable for an expert system to solve it.
Find the experts in task domain for the ES project.
Establish cost-effectiveness of the system.
Design the System
Identify the ES Technology
Know and establish the degree of integration with the other systems and databases.
Realize how the concepts can represent the domain knowledge best.
.

20. Continue:
Develop the Prototype
From Knowledge Base: The knowledge engineer works to −
Acquire domain knowledge from the expert.
Represent it in the form of If-THEN-ELSE rules.
Test and Refine the Prototype
The knowledge engineer uses sample cases to test the prototype for any deficiencies in
performance.
End users test the prototypes of the ES.
.

21. Continue:
Develop and Complete the ES
Test and ensure the interaction of the ES with all elements of its environment, including end
users, databases, and other information systems.
Document the ES project well.
Train the user to use ES.
Maintain the ES
Keep the knowledge base up-to-date by regular review and update.
Cater for new interfaces with other information systems, as those systems evolve.
.

22. Benefits of Expert Systems
Availability − They are easily available due to mass production of software.
Less Production Cost − Production cost is reasonable. This makes them affordable.
Speed − They offer great speed. They reduce the amount of work an individual puts in.
Less Error Rate − Error rate is low as compared to human errors.
Reducing Risk − They can work in the environment dangerous to humans.
Steady response − They work steadily without getting motional, tensed or fatigued.
Performance, Multiple expertise, Intelligent database
.

23. Disadvantage
Cost to buy and set up the system
Lacks the human touch
Expert systems have no common sense.
.

24. .

25. Thank you …
.