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MIS
CHAPTER 13
INTELLIGENT
INFORMATION SYSTEMS
Hossein BIDGOLI
MIS, Chapter 13
©2014 Cengage Learning
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Chapter 13 Intelligent Information Systems
learning outcomes
LO1
Define artificial intelligence, and explain how AI
technologies support decision making.
LO2
Describe an expert system, its applications, and its
components.
LO3
LO4
Describe case-based reasoning.
LO5
Describe fuzzy logic and its uses.
MIS, Chapter 13
©2014 Cengage Learning
Summarize the types of intelligent agents and how
they are used.
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Chapter 13 Intelligent Information Systems
l e a r n i n g o u t c o m e s (cont’d.)
LO6
LO7
LO8
Explain artificial neural networks.
LO9
Summarize the advantages of integrating AI
technologies into decision support systems.
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©2014 Cengage Learning
Describe how genetic algorithms are used.
Explain natural-language processing and its
advantages and disadvantages.
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Chapter 13 Intelligent Information Systems
What Is Artificial Intelligence?
• Artificial intelligence (AI)
– Consists of related technologies that try to simulate
and reproduce human thought and behavior
– Includes thinking, speaking, feeling, and reasoning
• AI technologies
– Concerned with generating and displaying knowledge
and facts
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Chapter 13 Intelligent Information Systems
What Is Artificial Intelligence? (cont’d.)
• Knowledge engineers try to discover “rules of
thumb”
– Enable computers to perform tasks usually handled
by humans
• Capabilities of these systems have improved in
an attempt to close the gap between artificial
intelligence and human intelligence
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Chapter 13 Intelligent Information Systems
AI Technologies Supporting Decision Making
• Decision makers use information technologies in
decision-making analyses:
– What-is
– What-if
• Other questions:
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–
–
–
Why?
What does it mean?
What should be done?
When should it be done?
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Table 13.1
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Applications of AI Technologies
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Chapter 13 Intelligent Information Systems
Robotics
• One of the most successful applications of AI
• Perform well at simple, repetitive tasks
• Currently used mainly on assembly lines in
Japan and the United States
• Cost of industrial robots
• Some robots have limited vision
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Chapter 13 Intelligent Information Systems
Robotics (cont’d.)
• Honda’s ASIMO
– One of the most advanced and most popular robots
– Works with other robots in coordination
• Personal robots
– Limited mobility/vision, and some speech capabilities
• Robots have some unique advantages in the
workplace compared with humans
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Chapter 13 Intelligent Information Systems
Expert Systems
• One of the most successful AI-related
technologies
• Mimic human expertise in a field to solve a
problem in a well-defined area
• Consist of programs that mimic human thought
behavior
– In a specific area that human experts have solved
successfully
• Work with heuristic data
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Chapter 13 Intelligent Information Systems
Components of an Expert System
• Knowledge acquisition facility
• Knowledge base
– Factual knowledge
– Heuristic knowledge
– Meta-knowledge
• Knowledge base management system
(KBMS)
• User interface
• Explanation facility
• Inference engine
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Exhibit 13.1
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An Expert System Configuration
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Chapter 13 Intelligent Information Systems
Components of an Expert System (cont’d.)
• Forward chaining
– Series of “if-then-else”
– Condition pairs are performed
• The “if” condition is evaluated first
• Then the corresponding “then-else” action is
carried out
• Backward chaining
– Starts with the goal—the “then” part
– Backtracks to find the right solution
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Chapter 13 Intelligent Information Systems
Components of an Expert System (cont’d.)
• Semantic (associative) networks
– Represents information as links and nodes
• Frames
– Store conditions or objects in hierarchical order
• Scripts
– Describe a sequence of events
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Chapter 13 Intelligent Information Systems
Uses of Expert Systems
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•
•
•
•
•
•
•
Airline industry
Forensics lab work
Banking and finance
Education
Food industry
Personnel management
Security
US Government
Agriculture
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Chapter 13 Intelligent Information Systems
Criteria for Using Expert Systems
• Human expertise is needed but one expert can’t
investigate all the dimensions of a problem
• Knowledge can be represented as rules or
heuristics
• Decision or task has already been handled
successfully by human experts
• Decision or task requires consistency and
standardization
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Chapter 13 Intelligent Information Systems
Criteria for Using Expert Systems (cont’d.)
• Subject domain is limited
• Decision or task involves many rules and
complex logic
• Scarcity of experts in the organization
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Chapter 13 Intelligent Information Systems
Criteria for Not Using Expert Systems
•
•
•
•
Very few rules
Too many rules
Well-structured numerical problems are involved
Problems are in areas that are too wide and
shallow
• Disagreement among experts
• Problems require human experts
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Chapter 13 Intelligent Information Systems
Advantages of Expert Systems
• Never become distracted, forgetful, or tired
• Duplicate and preserve the expertise of scarce
experts
• Preserve the expertise of employees who are
retiring or leaving an organization
• Create consistency in decision making
• Improve the decision-making skills of nonexperts
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Chapter 13 Intelligent Information Systems
Case-Based Reasoning
• Problem-solving technique
• Matches a new case (problem) with a previously
solved case and its solution stored in a database
• If there’s no exact match between the new case
and cases stored in the database
– System can query the user for clarification or more
information
• If no match is found after the above query
– Human expert must solve the problem
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Chapter 13 Intelligent Information Systems
Intelligent Agents
• Bots (short for robots)
– Software capable of reasoning and following rulebased processes
• Are becoming more popular
– Especially in e-commerce
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Chapter 13 Intelligent Information Systems
Intelligent Agents (cont’d.)
• Characteristics:
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Adaptability
Autonomy
Collaborative behavior
Humanlike interface
Mobility
Reactivity
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Chapter 13 Intelligent Information Systems
Intelligent Agents (cont’d.)
• Web marketing
– Collect information about customers, such as items
purchased, demographic information, and expressed
and implied preferences
• “Virtual catalogs”
– Display product descriptions based on customers’
previous experiences and preferences
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Chapter 13 Intelligent Information Systems
Shopping and Information Agents
• Help users navigate through the vast resources
available on the Web
• Provide better results in finding information
• Examples
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Pricewatch
BestBookBuys
www.mysimon.com
DogPile
• Searches the Web by using several search engines
• Removes duplicate results
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Chapter 13 Intelligent Information Systems
Personal Agents
• Perform specific tasks for a user
• Such as:
– Remembering information for filling out Web forms
– Completing e-mail addresses after the first few
characters are typed
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Chapter 13 Intelligent Information Systems
Data-Mining Agents
• Work with a data warehouse
• Detect trend changes
• Discover information and relationships among
data items that were not readily apparent
• Having this information early enables decision
makers to come up with a solution that
minimizes the negative effects of the problem
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Chapter 13 Intelligent Information Systems
Monitoring and Surveillance Agents
• Track and report on computer equipment and
network systems
– To predict when a system crash or failure might occur
• Example: NASA’s Jet Propulsion Laboratory
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Chapter 13 Intelligent Information Systems
Fuzzy Logic
• Allows a smooth, gradual transition between
human and computer vocabularies
• Deals with variations in linguistic terms by using
a degree of membership
• Designed to help computers simulate vagueness
and uncertainty in common situations
• Works based on the degree of membership in a
set
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Exhibit 13.3
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Degree of Membership in a Fuzzy System
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Chapter 13 Intelligent Information Systems
Uses of Fuzzy Logic
• Used in:
– Search engines, chip design, database management
systems, software development, and more
• Examples:
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Dryers
Refrigerators
Shower systems
TVs
Video camcorders
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Chapter 13 Intelligent Information Systems
Artificial Neural Networks
• Networks that learn and are capable of
performing tasks that are difficult with
conventional computers
• Examples:
– Playing chess, recognizing patterns in faces and
objects, and filtering spam e-mail
• Used for poorly structured problems
• Uses patterns
– Not the if-then-else rules that expert systems use
• Creates a model based on input and output
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Exhibit 13.4
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©2014 Cengage Learning
Artificial Neural Network Configuration
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Chapter 13 Intelligent Information Systems
Artificial Neural Networks (cont’d.)
• Used for many tasks, including:
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Bankruptcy prediction
Credit rating
Investment analysis
Oil and gas exploration
Target marketing
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Chapter 13 Intelligent Information Systems
Genetic Algorithms
• Used mostly in techniques to find solutions to
optimization and search problems
• Applications:
– Jet engine design, portfolio development, and
network design
• Find the combination of inputs that generates
the most desirable outputs
• Techniques
– Selection or survival of the fittest
– Crossover
– Mutation
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Chapter 13 Intelligent Information Systems
Natural Language Processing
• Developed so that users can communicate with
computers in human language
• Provides question-and-answer setting that’s
more natural and easier for people to use
• Products aren’t capable of a dialogue that
compares with conversations between humans
– However, progress has been steady
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Table 13.2
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NLP Systems
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Chapter 13 Intelligent Information Systems
Natural Language Processing (cont’d.)
• Categories:
– Interface to databases
– Machine translation
– Text scanning and intelligent indexing programs for
summarizing large amounts of text
– Generating text for automated production of standard
documents
– Speech systems for voice interaction with computers
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Chapter 13 Intelligent Information Systems
Natural Language Processing (cont’d.)
• Interfacing:
– Accepting human language as input
– Carrying out the corresponding command
– Generating the necessary output
• Knowledge acquisition:
– Using the computer to read large amounts of text and
understand the information well enough to:
• Summarize important points and store information
so the system can respond to inquiries about the
content
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Chapter 13 Intelligent Information Systems
Integrating AI Technologies into Decision Support
Systems
• I-related technologies can improve the quality of
decision support systems (DSSs)
– Including expert systems, natural language
processing, and artificial neural networks
• You can add AI technologies to a DSS’s model
base component
• Integrating expert system capabilities into the
user interface component can improve the
quality and user friendliness of a DSS
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Chapter 13 Intelligent Information Systems
Summary
• Intelligent information systems
– AI technologies are used to support decision-making
processes
• Expert systems
– Components
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Case-based reasoning
Intelligent agents
Fuzzy logic and genetic algorithms
Natural language processing
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