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2009-06-16 Expert Systems and Neural Networks

posted Jun 15, 2009, 3:30 PM by Unknown user   [ updated Jul 17, 2009, 8:27 AM by Eddie Woo ]

How Expert Systems Work

Expert systems have a knowledge base of rules (domain-specific heuristics) that govern situations and hence inform decisions.

  • Domain-specific: about one specific area
  • Heuristics: stategies that always seem to work

Expert systems are designed to imitate experts. Why would you mimic human experts with a computer system when there are real humans? Well...


  • Consistency can be an advantage
    • Repetitive situations can be responded to with consistency, regularity, predictability
    • Repetitive situations do not bore them or waste their presence
  • Decisions are clear as they come from pre-programmed rules and logic instead of intuition
  • Greater processing speed with simple data
  • Handle more data to superhuman levels of memory
  • Data can be stored indefinitely and can be accessed instantly
  • Humans can die, and humans are considered less disposable than machinery
  • Humans may be incapable in emotional stress
  • Greater availability in time and space + multiple users
  • Can replace original human expert(s) after their death(s)
  • Diligence (expert systems do not forget factors)
  • Persistence (machines do not sleep, they sleep mode!)


  • 'Dumb' as in expert systems lack common sense and intuition (Google Maps)
  • Boring-ness (cannot make creative responses)
  • Humans can think faster in complex, abstract situations
  • Humans learn
  • Machinery requires an electrical supply
  • Interpretation/inaccuracy (expert systems are limited by the situation data input - ambiguous)
  • Stubborn (does not automatically adjust or adapt to changes)

How Neural Networks Work

To be continued...