The document discusses expert systems, including their definition as computer systems that emulate human expert decision making. It describes how expert systems aim to capture specialized human knowledge in domains like medicine, computer configuration, and oil exploration. The key components of an expert system are discussed as the knowledge base, user interface, inference engine, explanation facility, knowledge acquisition facility, and external interface. Expert system shells are also mentioned as tools that provide the architecture and programming environment for developing expert systems.

1. MODULE IV
Expert System and Learning
B.Tech.(AI), IV
Artificial Intelligence (CSE 401)
Amity School of Engineering and Technology
Dr. Nidhi Mishra, Assistant Professor
Department of CSE, ASET

2. LEARNING OUTCOMES
• Students will be able to know about Expert System and
their importance in AI
• Students will be able to understand knowledge
acquisition, process of knowledge acquisition

3. What is an expert system?
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“An expert system is a computer system that
emulates, or acts in all respects, with the decision-
making capabilities of a human expert.”
Professor Edward Feigenbaum
Stanford University

4. 4
• Expert systems aimed to capture specialist human expertise
which was in short supply. Eg..,
– Medical expertise
– Computer configuration expertise.
– Expertise for oil exploration.
• Aim was to develop systems capturing this expertise, so the
knowledge could be deployed where experts were unavailable.
• Expert system (ES) are knowledge intensive programs that solve
problems in a domain that requires considerable amount of
technical expertise.

5. 5
An Expert System (ES) is a computer-based system (mainly
software) that uses knowledge and facts, and apply an appropriate
reasoning technique (inferencing) to solve problems in a given
field (domain) that normally require the services of human
experts.

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• Attempt to Imitate Expert Reasoning Processes and knowledge
in Solving Specific Problems
• Most Popular Applied AI Technology
– Enhance Productivity
– Augment Work Forces
• Narrow Problem-Solving Areas or Tasks
• Provide Direct Application of Expertise
• Expert Systems Do Not Replace Experts, But They
– Make their Knowledge and Experience More Widely
Available
– Permit Non experts toWork Better

7. Need & Justification for expert
systems- cognitive problems
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• Expert systems are suitable for knowledge intensive problems
that are typically solved by human experts.
• Because expert systems depend on human knowledge, if
human experts are unable to solve a given problem, no
successful expert system can be developed to solve the problem
either.
• When the demand for human expertise surpasses the
availability of experts, an expert system may be the tool for
handling the situation.
• The justification of using an expert system for a selected
problem depends on the primary goal of the organization and
the types of alternatives available.

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9. Characteristics of Expert System
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11. Brief history
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16. Basic Concept of ES
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18. Designing Expert System
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19. Architecture of Expert System
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21. 1. Knowledge Base
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22. Using the Knowledge Base
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23. 2. User Interface
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24. 3. Inference Engine
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25. 4. Explanation facility
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26. 5. Knowledge Acquisition facility
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27. 6. External interface
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This provides the communication link between the ES and the external
environment.

28. Expert System Shells
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29. Expert System Shells
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