Expert Systems and Their Applications

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Expert Systems and Their Applications

• John Paxton
• Montana State University
• August 14, 2003

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Bozeman
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Definitions

• A model and associated procedure that exhibits,
  within a specific domain, a degree of expertise
  in problem solving that is comparable to that of
  a human expert. (Ignizio)
• An expert system is a computer system which
  emulates the decision-making ability of a human
  expert. (Giarratono)

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Characteristics

• Operates in a narrow domain
• Separates knowledge from processing
• Can explain how a particular conclusion is
  reached
• Can explain why specific data is needed
• Permits inexact reasoning
• Can make mistakes
• Changes are easy to incorporate

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Components

• Program Algorithm Data Structure
• Expert System Inference Engine Knowledge

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Usage (2002)
Area Percentage
Production/Ops Mgmt 48
Finance 17
Information Systems 12
Marketing/Transactions 10
Accounting/Auditing 6
International Business 3
Human Resources 2
Other 2
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Why Use an Expert System?

1. Helps preserve knowledge--builds up the corporate
   memory of the firm.
2. Helps if expertise is scarce, expensive, or
   unavailable.
3. Helps if under time and pressure constraints.
4. Helps in training new employees.
5. Helps improve worker productivity.

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Architecture
KNOWLEDGE BASE
USER INTERFACE
INFERENCEENGINE
WORKINGMEMORY
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Knowledge Base

• Contains facts
• antacid (Imodium)
• Contains rules
• if traveler (x) and stomach-pains (x) then
  take (y, antacid (y))

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Inference Engine

• Rules that match working memory are identified
  and then fired.
• This updates working memory and the knowledge
  base.
• The process is repeated.

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Inference Engine

• Conflict Resolution
• fire all matching rules
• fire the first matching rule
• fire the highest priority matching rule
• fire the most specific rule
• fire the rule that uses the most recent data

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Inference Engine

• Forward Chaining. Starting with the data, a
  conclusion is reached. cat (Mulder) cat
  (x) ? mammal (x)
• Backward Chaining. Starting with a hypothesis,
  it works backwards to the data.

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Uncertainty Sources

• Weak implications
• Imprecise language (e.g. often)
• Unknown data
• Combining views of different experts

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Uncertainty

• Certainty Factors.
• Dempster-Shafer Theory.
• Bayesian Networks.
• Fuzzy Logic.

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Certainty Factors

• IF the light is greenTHEN it is ok to cross the
  street cf 0.9
• easy to compute
• easy to propagate
• - somewhat ad hoc
• - all certainty factors are independent

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Bayesian Reasoning

• Based on Bayes Theorem and standard probability
  theory
• P(HE) P(EH) P(H) / P(E)

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Birthday Surprise

• What is the probability that 2 people in a room
  of 30 share a birthday?
• P 1 365/365 364/365 336/365 0.70

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Fuzzy Logic

1. Fuzzification (120 kph 0.95 fast)
2. Inference (IF speed is fast THEN stopping
   distance is short)
3. Composition (0.8 short and 0.7 short 0.7
   short)
4. Defuzzification (0.7 short 20 meters)

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People Involved

• Domain Expert
• Knowledge Engineer
• Programmer
• Project Manager
• End User

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Building an Expert System

• Problem assessment
• determine the problems characteristics
• identify the main participants
• specify the projects objectives
• determine the resources needed
• Data and knowledge acquisition
• collect and analyze data and knowledge
• make key concepts of the system design explicit

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Building an Expert System

• Development of a prototype system
• choose a tool
• transform data and represent knowledge
• design and implement prototype
• test the prototype
• Development of a complete system
• prepare a detailed design for a full scale system
• collect additional data and knowledge
• develop the user interface
• implement the complete system

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Building an Expert System

• Evaluation and revision of a complete system
  (look for inconsistencies and incompleteness)
• Integration and maintenance of system
• make arrangements for technology transfer
• establish an effective maintenance program

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Building a Fuzzy Expert System

1. Specify the problem. Define linguistic
   variables.
2. Determine the fuzzy sets.
3. Construct the fuzzy rules.
4. Encode the fuzzy sets, fuzzy rules and fuzzy
   inference procedures into the expert system.
5. Evaluate and tune the system.

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Expert System Shell

• CLIPS is a productive development and delivery
  expert system tool which provides a complete
  environment for the construction of rule and/or
  object based expert systems. Created in 1985,
  CLIPS is now widely used throughout the
  government, industry, and academia.

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CLIPS features

• Allows for many types of knowledge representation
  (e.g. rules and procedures)
• Portable (written in C)
• Extensible
• Embeddable
• Interactive Development
• Verification and Validation support
• Fully documented
• Public Domain!

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Advantages

• Natural Language representation
• Uniform structure
• Separates knowledge from processing
• Can deal with incomplete and uncertain knowledge

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Disadvantages

• Opaque relations between rules
• Ineffective search strategy
• Typically cant learn

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Commercial Applications

• National Semiconductor Manufacturing (Singapore)
  troubleshoot recurrent equipment breakdowns
• Work and Income New Zealand (a.k.a. Social
  Welfare Department) - deal with questions of
  eligibility, allowances and benefit amounts
• GE Capital Global Consumer Finance - help
  identify risk, retain customers and target
  prospects

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Commercial Applications

• Department of Industry and Fisheries, Tasmania
  assist the delivery of information to farmers
• Misselbrook and Weston stores detect in-store
  fraud
• Channel 4 TV (UK) sequence commercial breaks
• Tokyo Nissan - how to increase domestic demand

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Commercial Applications

• Rockwell Aerospace and NASA - enables the user to
  quantify molecular and particulate contamination
  requirements for solar arrays, thermal control
  surfaces, or optical sensors
• Meiji Mutual Life Insurance Company - select the
  most suitable product, along with a reason for
  the choice, from Meiji's range of 37 individual
  oriented products

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Questions?