 Information Technology Based on AI
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What is Artificial Intelligence?
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Artificial Intelligence vs. Natural Intelligence
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Corporate Applications of Artificial Intelligence
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Concept and Applications of an Expert System
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Characteristics and Examples of an Expert System
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Structure of an Expert System
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Development of an Expert System
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Fuzzy Logic
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Neural Networks
AI-based Information Technology
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 What is Artificial Intelligence?
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Intelligence simulated after man’s intelligence by the use of a computer
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Natural Intelligence vs. Artificial Intelligence
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A science that uses a computer technoloogy to mimic human’s logical
behavior
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Rational/logical behavior:
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e.g., evaluation, inferencing, judgment, problem-solving, etc.
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 Knowledge Proc. vs. Information Proc.
Knowledge Processing
Information Processing
Data, Concepts, Judgments
Input
Data
Knowledge Base
(Data Base access possible)
Built-in Base
Data Base
Symbol-oriented
(doesn’t need algorithms)
Processing
Numerically-oriented
(uses algorithms)
Solutions/decisions, explanations
Output
Numeric information/data
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 Artificial Intelligence Vs. Natural Intelligence
Attributes
Natural Intelligence
Artificial Intelligence
Knowledge Duplication
Hard
Easy
Cost of Knowledge
Expensive
Cheap
Amount of Possessed
Knowledge
Limited
Nearly limitless
Knowledge Retention
Temporary (Volatile)
Permanent
Consistency
Low
High
Flexibility
High
Low
Creativity
High
Low
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Corporate Applications of AI
● Industrial
● Natural
Robots
Language Processing
● Expert
Systems
● Visual
Recognition
● Machine
Learning
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Fuzzy Logic
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Neural Networks
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What is an Expert System
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Definition: A computer program that represents in a computer knowledge
related to a specific problem domain and uses it to solve problems like a
human expert.
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Applied to diagnosis, prediction, design, interpretation, planning, etc.
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Can be helpful for experts as well as novices
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Key capabilities:
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inferencing
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drawing conclusions from the results of inferencing
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providing explanations for inferencing
Business applications: tax advising, personnel recruiting, investment, etc.
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 Categories of Expert Systems
Category
Task
Classification
Identifies an object based on given characteristics
Diagnosis
Inferring system malfunctions from observations
Monitoring
Comparing observations to plans, flagging exceptions
Process Control
Scheduling/Planning
Interpreting, predicting, reparing, and monitoring systems
behavior
Configuring objects under constraints (e.g.,
specifications)
Developing plans to achieve goal(s)
Prediction
Inferring likely consequences of given situations
Design
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 ES(Expert System) Vs. DSS
Attributes
Expert Systems
Decision Support Systems
Goal
To replace an expert
To assist a decision maker
Who makes a decision?
System
User & system
Key role
Offering advice based on
expertise
Decision analysis
Who asks questions
Machine
User
Data manipulation
Symbol-oriented
Numerically-oriented
Problem domain
Narrow, specific
Complex, broad
What problems are supported
Routine problems
Ad hoc problems
Support base
Procedural/factual
knowledge(KB)
Factual knowledge (DB)
Reasoning capability
Limited
No
Explanation capability
Yes
Simple, limited
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Characteristics of Expert Systems
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Based on knowledge
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Uses qualitative, rather than quantitative, information
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Makes use of inferencing to draw conclusions
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Uses experts’ heuristics
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Provides explanations for reasoning
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Can function even when some data are missing
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Capable of handling uncertain situations
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Support for a narrow problem domain
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 Examples of ES Uses
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Credit Card Approval at American Express
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The Authorizer’s Assistant
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determines whether to approve purchases made
with an American Express credit card while the
customer is awaiting to make payment
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The system handles the majority of credit card
approval work, whereas the experts evaluate the
remaining 5 %
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The productivity of the approval experts has
increased by 20%
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 Examples of ES Uses - Cont’d
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Ticket Auditing at Northwest Airlines
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When Northwest Airlines acquired Republic
Airlines, its volume of operations increased to
70,000 tickets per day, that had to be audited
manually.
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Auditing involved a very time-consuming task
of checking fare information on a copy of each
ticket
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A ticket auditing expert system was developed
in 1990
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The system now audits 100% of the tickets.
The errors made by travel agencies have
decreased significantly, and as much as 10
million dollars is saved each year as a result of
using the system.
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 Examples of ES Uses - Cont’d
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Commercial Loan Analysis at a Bank
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Developed by a bank that specializes in
loans in excess of 30 million dollars
(typically for construction projects)
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Usually a 6-month study costing 1 quarter
million dollars is required to determine the
approvability of the loan
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An expert system is developed to replace
the costly study.
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The system classifies a loan into three
categories: Approve, Reject, Gray-area.
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Loan officers should take care of only the
Gray-area loans, thereby significantly
reducing the loan evaluation costs.
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 The Structure of an Expert System
User
User
Problem-related
facts (data)
User
Interface
Knowledge
Engineer
Explanation
Module
Consulting Environment
(System Use)
Knowledge
Acquisition
Modulle
Inference
Engine
Blackboard
(Workspace)
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Knowledge
Base
Development Environment
(Knowledge Acquisition)
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 Development of an Expert System
Project Initialization
Problem definition; Recognition of needs for a system
System Design
Development methods/tools identified; system design
Prototype
Development
Complete System
Development
Implementation/
Installation
Maintenance
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A conceptual prototype constructed
KB construction; testing
User acceptance; installation; User training
Operation; modifications; maintenance
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 Methods for Developing Expert Systems
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Custom Development
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Can create a system suited to the specific needs of an organization
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Is time consuming and requires lots of resources
Expert System Shell
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Uses a shell to construct a system with more ease over a short time period
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The emphasis is placed upon the transfer of acquired knowledge by the user to the
system
Off-the-shelf Package
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Can simply purchase a ready-made package and use it with minor modification
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Can cost less and avoid the complex development project
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 Exsys Professional: An ES Shell Illustration
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 Fuzzy Logic
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A technique that deals with uncertainties by simulating the process of human
reasoning, allowing the computer to behave less precisely and logically than
conventional computers do.
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e.g., tall, somewhat tall, really tall, very tall, etc.
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Sample fuzzy logic applications:
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vacuum cleaners
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air conditioners
An example of a fuzzy logic rule:
IF the temperature is very hot AND the water level is somewhat low
THEN add cold water to the container.
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Neural Networks
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A computer-based technology that is based on the research related to the human
brain and neural system
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Can process large amounts of information concurrently
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Can use the learning function of the human brain to classify information, based on
data of past experience
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Neural networks
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A model created after the biological neural networks
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The human brain is composed of hundreds of billions of neurons, which are
interconnected with one another in a sophisticated manner.
Primary advantages of the neural network technique:
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Don’t have to rely on the predefined problem-solving knowledge, but seeks a solution
based on a vast amount of data
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 Components of a Neural Network
Output layer
PE
PE
PE
Hidden layer
Input layer
PE
PE
PE
PE
PE
PE
PE: Processing Element (처리요소)
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