This document discusses expert systems, which are computer applications that embody expertise to solve problems in specific domains. It defines expert systems as consisting of an inference engine and knowledge base, similar to how traditional programs have algorithms and data structures. Some key points made: - Expert systems can be used for tasks like diagnosis, financial planning, and configuring computers that previously required human expertise. - The major components of expert systems are the user interface, inference engine, and knowledge base. - Knowledge acquisition is the process of extracting an expert's knowledge and structuring it for the knowledge base. - Example expert systems include MYCIN for medical diagnosis and DART for computer fault diagnosis.

1. Topic: Expert Systems
CS6659 Artificial Intelligence
III YEAR VI SEM
Presented by,
Ms. M. Rupa
, AP/CSE,JCTCET

2. Motivation & Objectives
• Utilization of computers to deal with knowledge
• Quantity of knowledge increases rapidly
• Knowledge might get lost if not captures
• Computers have special requirements for dealing
with knowledge
• Some knowledge related tasks can be solved better
by computers than by humans

3. INTRODUCTION
Expert systems are computer applications which
embody some non-algorithmic expertise for
solving certain types of problems.
For example :
• Diagnostic applications
• Play chess
• Financial planning decisions
• Configure computers
• Monitor real time systems
• Underwrite insurance policies
• Perform many services which previously
required human expertise.

5. What is Expert System ?
• An expert system, is an interactive computer-
based decision tool that uses both facts and
heuristics to solve difficult decision making
problems, based on knowledge acquired from an
expert.
• Inference engine + Knowledge = Expert system
( Algorithm + Data structures = Program
in traditional computer )
• First expert system, called DENDRAL, was
developed in the early 65's at Stanford University.

9. Roles of Individuals who interact
with the system
• Domain expert : The individuals who currently are
experts in solving the problems; here the system is
intended to solve.
• Knowledge engineer : The individual who encodes
the expert's knowledge in a declarative form that can
be used by the expert system.
• User : The individual who will be consulting with the
system to get advice which would have been
provided by the expert.
• System engineer : builds the user interface, designs
the declarative format of the knowledge base, and
implements the inference engine.

10. User Interface
Inference
Engine
Knowledge
Base
Three Major ES Components

11. Components and Interfaces
• User interface : The code that controls the dialog
between the user and the system.
• Inference engine : The code at the core of the
system which derives recommendations from the
knowledge base and problem specific data in
working storage.
• Knowledge base : A declarative representation of
the expertise often in IF THEN rules .

12. Expert System Benefits
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.

13. Expert System 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 includes −
Limitations of the technology
Difficult knowledge acquisition
ES are difficult to maintain
High development costs

14. Applications of Expert System

15. Knowledge Acquisition
• Knowledge acquisition is the process of extracting,
structuring and organizing knowledge from one source,
usually human experts, so it can be used in software
such as an ES.

17. Typical expert systems

18. Typical expert systems
MYCIN was an early backward chaining expert system that used artificial
intelligence to identify bacteria causing severe infections. It is used in medical
field.

19. DART
DART: Expert systems for automated computer fault diagnosis.
The Dynamic Analysis and Replanning Tool, explores the
application of artificial intelligence techniques to the diagnosis of
computer faults. It assists a technician in finding the faults in a
computer system. (hardware and software).
DART uses a device-independent language for describing devices
and device-independent inference procedure for diagnosis.
The primary goal of the DART Project is to develop programs that
capture the special design knowledge and diagnostic abilities of
these experts and to make them available to field engineers.

20. XCON
• XCON – (eXpert CONfigure) is a type of computer program used by
VAX – ("virtual address extension") or (logical address) Computers for
selecting various components based on customers requirements.

21. Expert System Shells
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.
A shell is a piece of software which contains the user interface, a
format for declarative knowledge in the knowledge base, and an
inference engine.
Many expert systems are built with products called expert system
shells.
The knowledge and system engineers uses these shells in making
expert systems.

23. • Knowledge engineer : uses the shell to build a
system for a particular problem domain.
• System engineer : builds the user interface,
designs the declarative format of the knowledge
base, and implements the inference engine.
Depending on the size of the system, the
knowledge engineer and the system engineer
might be the same person.

24. Outcome of Human Expert Behaviors in ES
• Recognize and formulate the problem
• Solve problems quickly and properly
• Explain the solution
• Learn from experience
• Restructure knowledge
• Break rules
• Determine relevance
• Degrade gracefully