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this presentation provides an introduction to the expert systems.

this presentation provides an introduction to the expert systems.

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  • 1. EXPERT SYSTEMS BY MEHWISH MANZER (63) MEER SADAF NAEEM (58) DUR-E-MALIKA (55)
  • 2. Overview
    • What is an Expert System?
    • History
    • Components of Expert System
    • Who is involved?
    • Development of Expert System
  • 3. WHAT IS AN EXPERT SYSTEM?
    • An expert system is a computer program that contains some of the subject-specific knowledge of one or more human experts.
  • 4. History of Expert Systems
  • 5.
    • Early 70s
    • Goal of AI scientists  develop computer programs that could in some sense think .
    • In 60s  general purpose programs were developed for solving the classes of problems but this strategy produced no breakthroughs.
    • In 1970  it was realized that The problem-solving power of program comes from the knowledge it possesses.
  • 6. To make a program intelligent, provide it with lots of high-quality, specific knowledge about some problem area.
  • 7. Building Blocks of Expert System
  • 8.
    • Knowledge base (facts)
    • Production Rules ("if.., then..")
    • Inference Engine (controls how "if.., then.." rules are applied towards facts)
    • User Interface
  • 9. Knowledge Base
    • The component of an expert system that contains the system’s knowledge.
    • Expert systems are also known as Knowledge-based systems.
  • 10. Knowledge Representation
    • Knowledge is represented in a computer in the form of rules ( Production rule).
    • Consists of an IF part and THEN part.
    • IF part lists a set of conditions in some logical combination.
    • If the IF part of the rule is satisfied; consequently, the THEN part can be concluded.
  • 11. Knowledge Representation
    • If flammable liquid was spilled then call the fire department.
    • If the material is acid and smells like vinegar then the spill material is acetic acid.
  • 12.
    • Chaining of IF-THEN rules to form a line of reasoning
    • Forward chaining (facts driven)
    • Backward chaining (goal driven)
  • 13. Inference Engine
    • An inference engine tries to derive answers from a knowledge base.
    • It is the brain of the expert systems that provides a methodology for reasoning about the information in the knowledge base, and for formulating conclusions.
  • 14. User Interface
    • It enables the user to communicate with an expert system.
  • 15. Other features
    • Reasoning with uncertainty
    • Explanation of the line of reasoning
    • Fuzzy Logic
  • 16. Who is involved? ?
  • 17. Knowledge Engineer
    • A knowledge engineer is a computer scientist who knows how to design and implement programs that incorporate artificial intelligence techniques.
  • 18. Domain Expert
    • A domain expert is an individual who has significant expertise in the domain of the expert system being developed.
  • 19. Knowledge Engineering
    • The art of designing and building the expert systems is known as KNOWLEDGE ENGINEERING knowledge engineers are its practitioners.
    • Knowledge engineering relies heavily on the study of human experts in order to develop intelligent & skilled programs.
  • 20. Developing Expert Systems
    • Determining the characteristics of the problem.
    • Knowledge engineer and domain expert work together closely to describe the problem.
  • 21.
    • The engineer then translates the knowledge into a computer-usable language, and designs an inference engine, a reasoning structure, that uses the knowledge appropriately.
    • He also determines how to integrate the use of uncertain knowledge in the reasoning process, and what kinds of explanation would be useful to the end user.
  • 22.
    • When the expert system is implemented, it may be:
        • The inference engine is not just right
        • Form of representation of knowledge is awkward
    • An expert system is judged to be entirely successful when it operates on the level of a human expert.
  • 23. Human Expertise vs Artificial Expertise
    • Perishable
    • Difficult to transfer
    • Difficult to document
    • Unpredictable
    • Expensive
    • Permanent
    • Easy to transfer
    • Easy to document
    • Consistent
    • Affordable
  • 24. Some Prominent Expert Systems
    • Dendral
    • Dipmeter Advisor
    • Mycin
    • R1/Xcon
  • 25. THE END THANK YOU