An expert system is software that attempts to reproduce the performance of one or more human experts in a specific problem domain. It contains a knowledge base of rules, an inference engine to manipulate the rules, and a user interface. Early expert systems were created in the 1970s and proliferated in the 1980s, being among the first truly successful forms of AI software. They derive their power from the knowledge in their knowledge bases rather than specific formalisms.

1. A
SEMINAR PRESENTATION
ON
“EXPERT SYSTEM”
Seminar Guide Submitted By:
Mr. Mahendra Singh Sagar Deepak Kumar
Assistant Professor Roll No.: TCA1405019
Master of Computer Application
4th Sem. (LT)
COLLEGE OF COMPUTING SCIENCE AND
INFORMATION TECHNOLOGY
(Teerthanker Mahaveer University, Delhi Road, Moradabad – 244001)

2. INTRODUCTION
An expert system is software that attempts to
reproduce the performance of one or more
human experts, most commonly in a specific
problem domain. An expert system is a
computer system that emulates the decision-
making ability of a human expert. Expert
systems are designed to solve complex
problems by reasoning about knowledge,
represented primarily as if-then rules rather than
through conventional procedural code. The first
expert systems were created in the 1970s and
then proliferated in the 1980s. Expert systems
were among the first truly successful forms
of AI software.

3. HISTORY
Edward Feigenbaum in a 1977 paper said that the key
insight of early expert systems was that "intelligent
systems derive their power from the knowledge they
possess rather than from the specific formalisms and
inference schemes they use" (as paraphrased by
Hayes-Roth, et al.) Although, in retrospect, this seems a
rather straight forward insight, it was a significant step
forward at the time. Until then, research had been
focused on attempts to develop very general-purpose
problem solvers such as those described
by Newell and Simon. Expert systems were introduced
by the Stanford Heuristic Programming Project led by
Feigenbaum, who is sometimes referred to as the
"father of expert systems".

4. SOFTWARE ARCHITECTURE

5. CONTINUE
 Truth Maintenance. Truth maintenance systems
record the dependencies in a knowledge-base so
that when facts are altered dependent knowledge
can be altered accordingly. For example, if the
system learns that Socrates is no longer known to
be a man it will revoke the assertion that Socrates
is mortal.
 Hypothetical Reasoning. In hypothetical
reasoning, the knowledge base can be divided up
into many possible views, a.k.a. worlds. This allows
the inference engine to explore multiple
possibilities in parallel.

6. CONTINUE
 Fuzzy Logic. One of the first extensions of simply
using rules to represent knowledge was also to
associate a probability with each rule.
 Ontology Classification. With the addition of
object classes to the knowledge base a new type
of reasoning was possible. Rather than reason
simply about the values of the objects the system
could also reason about the structure of the objects
as well.

7. WHAT IS EXPERT SYSTEM
In AI, an expert system is a computer
system that emulates the decision-making
ability of a human expert. Expert systems
are designed to solve complex problems
by reasoning about knowledge,
represented primarily as if-then
rules rather than through
conventional procedural code. The first
expert systems were created in the 1970s
and then proliferated in the 1980s. Expert
systems were among the first truly
successful forms of AI software.

8. SECURITY
Experts in the field of computer security can
work in a variety of positions, including those as
network and computer systems administrators
or information security analysts. Network and
computer systems administrators maintain
computer and network security and update
security programs as necessary. Information
security analysts develop an organization's
computer security standards; install software
programs to protect information stored on
computers and monitor computer networks for
security breaches.

9. TYPES OF EXPERT SYSTEM
COMPONENTS
A computer program designed to model the
problem-solving ability of a human expert.
1. A knowledge base that contains the knowledge obtained
from one or more experts, generally in the form of rules.
2. An inference engine that manipulates the knowledge
found in the knowledge base to arrive at a solution.
3. A user interface that allows the user to query the system
and obtain the solution.
4. An explanation facility that explains the working of the
system: how the rules were derived, applied, and
sometimes the confidence levels that can be attached to
the results.

10. CONTINUE
Chaining
Inference rules are may forward chaining
and backward chaining. Forward chaining
starts with the data available, and uses the
inference rules to extract more data until a
desired goal is reached. Backward
chaining starts with a list of goals and
works backwards to see if data exist which
will allow it to conclude that any of these
goals is true.

11. CONTINUE
Real-time Adaption
Real-time expert systems, designed to adapt over time to
changing input data, are commonly necessary in process
control, network management and other dynamic
systems.
Learning Capabilities
Expert systems that learn from a storied history of
successful and failed solutions are more reliable, but can
be challenging to program.

12. ADVANTAGE AND DISADVANTAGE
ADVANTAGE
Consistent answers for repetitive decisions,
processes and tasks
Holds and maintains significant levels of
information
Encourages organizations to clarify the logic of
their decision-making
Never "forgets" to ask a question, as a human
might

13. CONTINUE
DISADVANTAGE
Lacks common sense
Cannot make creative responses as human expert
Domain experts not always able to explain their logic
and reasoning
Errors may occur in the knowledge base
Cannot adapt to changing environments