Expert%20Systems

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Expert Systems
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Expert Systems

• Programs which attempt to imitate the reasoning
  processes and knowledge of experts in solving
  specific problem
• Widely used in industry
• Apply a direct means of applying expertise
• purpose is to make expert knowledge and
  experience more widely available

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Knowledge Engineering

• The construction of expert systems is done by
  using knowledge engineering
• This involves
• knowledge acquisition - collection
• knowledge representation - organisation
  representation (IF-THEN rules)
• knowledge inferencing
• knowledge transfer

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Inferencing

• making up a new fact based on existing knowledge
• Unique feature of ES is ability to reason
• expertise is stored in knowledge base
• computer is programmed so that it can make
  inferences
• (drawing conclusion from knowledge base)
• This is performed in the inference engine
• includes procedures regarding problem solving

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Rules

• Most ES are rule based
• Knowledge is stored in the form of rules
• problem-solving procedures
• IF the engine is idle AND the fuel pressure is
  less than 38psi, AND the guage is accurate, THEN
  there is a fuel system fault

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Explanation Capability

• Unique ability to explain advice or
  recommendation
• subsystem called the justifier
• enables the system to examine its own reasoning
  and explain its operation

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Structure

• Development environment
• used to build the components and put knowledge
  into the knowledge base
• Consultation environment
• used by nonexperts to obtain expert knowledge and
  advice
• Improvement environment

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Knowledge Acquisition Subsystem

• Accumulation, transfer and transformation of
  problem-solving expertise from expert sources to
  a computer
• Knowledge engineers gather this expertise
• help the expert structure the problem area by
  interpreting and integrating human answers to
• questions
• drawing analogies
• posing counterexamples

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Knowledge Base

• Contains the knowledge necessary for
• understanding, formulating, and solving problems
• Includes facts and rules that use the knowledge
  to solve specific problems (heuristics)
• heuristics express the informal judgmental
  knowledge in an application area
• The information in the knowledge base is
  incorporated into a computer program by a process
  called knowledge representation

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Inference Engine

• Brain of the expert system (rule interpreter)
• program provides a methodology for reasoning
  about information in the knowledge base to
  formulate conclusions

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Explanation Subsystem

• Responsibility for conclusions can be traced by
  answering questions such as
• Why was a certain question asked by the expert
  system
• How was a certain conclusion reached
• Why was a certain alternative rejected

Knowledge Refining System

• Analysis the reasons for success and failure

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Development

• A tool used to expedite development is called an
  ES shell
• A shell can be used for many applications insert
  new knowledge base
• Include all the generic components of an ES, but
  they do not include the knowledge
• faster development plus the programming skill
  required is much lower

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Areas of use

• Prediction systems
• demographic predictions, economic forecasting
• Diagnostic systems
• medical, software diagnosis
• Monitoring systems
• compare observations of system behaviour with
  standards
• Control systems
• govern the overall behaviour of a system

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Reference

• Expert System applications in business A review
  and analysis of the literature
• Bo K.Wong, John A.Monaco
• Information and Management Vol 29 Part 3 1995
• HF 5548.125.15

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Benefits

• Increased output and productivity
• Accessibility to knowledge
• Decreased decision-making time
• Reduced downtime
• Capture of Scarce expertise
• Flexibility
• Elimination of the need for expensive equipment
• Operations in hazardous environments

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Benefits

• Increased Capabilities of other systems
• Integration of several experts opinions
• Ability to work with incomplete or uncertain
  information
• Provide training
• Improved decision making
• Transfer knowledge to remote locations

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Limitations

• Knowledge is not always readily available
• Expertise can be hard to extract from humans
• The approach of each expert to situation
  assessment may be different, yet correct
• It is hard, even for a highly skilled expert, to
  abstract good assessments under pressure
• ES work well only in a narrow domain of knowledge

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Limitations

• Vocabulary,jargon that experts use for expressing
  facts and relations is often limited and not
  understood by others
• Help is required from knowledge engineers who are
  rare and expensive
• lack of trust by end users