An expert system is software that attempts to reproduce the performance of human experts in a specific domain. It consists of a knowledge base of facts and rules, a working storage to apply those rules to specific problems, and an inference engine that derives solutions. Expert systems are designed by knowledge engineers who work with domain experts to encode their expertise for problems such as consistent decision making. While expert systems provide standardized answers and never forget questions, they lack common sense, creativity, and can have errors or difficulties adapting to changes.

1. A presentation on Expert System
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2. CONTENTS
• Introduction
• History
• Major Components
• Major Roles
• Advantages
• Disadvantages
• Applications
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3. 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.
• In artificial intelligence, 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.
• An expert system is divided into two sub-systems: the inference engine and the knowledge base. The
knowledge base represents facts and rules. The inference engine applies the rules to the known facts to
deduce new facts. Inference engines can also include explanation and debugging capabilities.
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4. History
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5. Major Components
• Knowledge base - a declarative representation
of the expertise, often in IF THEN rules
• Working storage - the data which is specific to
a problem being solved
• Inference engine - the code at the core of the
system
• Derives recommendations from the
knowledge base and problem-specific data
in working storage
• User interface - the code that controls the
dialog between the user and the system
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6. Major Roles
• Domain expert – currently experts solving the problems the system is intended to solve
• Knowledge engineer - encodes the expert's knowledge in a declarative form that can be used by
the expert system
• User - will be consulting with the system to get advice which would have been provided by the
expert
• Systems built with custom developed shells for particular applications:
• System engineer - the individual who builds the user interface, designs the declarative
format of the knowledge base, and implements the inference engine
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7. Advantages
• 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
• The goal of knowledge-based systems is to make the critical information required for the
system to work explicit rather than implicit.
• Ease of maintenance is the most obvious benefit.
• In a traditional computer program the logic is embedded in code that can typically only
be reviewed by an it specialist.
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8. Disadvantages
• 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
• The most common disadvantage cited for expert systems in the academic literature is
the knowledge acquisition problem.
• Obtaining the time of domain experts for any software application is always difficult but
for expert systems it was especially difficult because the experts were by definition
highly valued
• Performance was especially problematic because early expert systems were built using
tools such as lisp, which executed interpreted rather than compiled code.
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9. Applications
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