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Applications of Expert System
AI
The following table shows where ES can be
applied:-
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.
Applications of Expert System
Expert System Technology
Expert System Development Environment - The ES
development environment includes hardware and
tools. They are:-
Workstations, minicomputers, mainframes.
High level Symbolic Programming Languages
such as LISt Programming (LISP) and
PROgrammation en LOGique (PROLOG).
Large databases.
There are several levels of ES technologies available.
Expert systems technologies include:-
Tools − They reduce the effort and cost involved in
developing an expert system to large extent.
Powerful editors and debugging tools with
multi-windows.
They provide rapid prototyping
Have Inbuilt definitions of model, knowledge
representation, and inference design.
Shells − A shell is nothing but an expert system
without knowledge base. A shell provides the
developers with knowledge acquisition, inference
engine, user interface, and explanation facility.
For example, few shells are given below −
Java Expert System Shell (JESS) that provides
fully developed Java API for creating an expert
system.
Vidwan, a shell developed at the National
Centre for Software Technology, Mumbai in
1993. It enables knowledge encoding in the
form of IF-THEN rules.
Development of Expert Systems: General Steps
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.
The process of ES development is iterative. Steps in
developing the ES include:-
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.
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.
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.
AI- Applications of Expert System
Artificial Intelligence - Robotics
Artificial Intelligence - Neural
Networks
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Applications of expert system

  • 2. The following table shows where ES can be applied:- 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. Applications of Expert System
  • 3. Expert System Technology Expert System Development Environment - The ES development environment includes hardware and tools. They are:- Workstations, minicomputers, mainframes. High level Symbolic Programming Languages such as LISt Programming (LISP) and PROgrammation en LOGique (PROLOG). Large databases. There are several levels of ES technologies available. Expert systems technologies include:-
  • 4. Tools − They reduce the effort and cost involved in developing an expert system to large extent. Powerful editors and debugging tools with multi-windows. They provide rapid prototyping Have Inbuilt definitions of model, knowledge representation, and inference design. Shells − A shell is nothing but an expert system without knowledge base. A shell provides the developers with knowledge acquisition, inference engine, user interface, and explanation facility. For example, few shells are given below − Java Expert System Shell (JESS) that provides fully developed Java API for creating an expert system. Vidwan, a shell developed at the National Centre for Software Technology, Mumbai in 1993. It enables knowledge encoding in the form of IF-THEN rules.
  • 5. Development of Expert Systems: General Steps 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. The process of ES development is iterative. Steps in developing the ES include:-
  • 6. 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. 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.
  • 7. 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.
  • 8. AI- Applications of Expert System Artificial Intelligence - Robotics Artificial Intelligence - Neural Networks Topics for next Post Stay Tuned with