Download 9781111533960_PPT_ch13

MIS
CHAPTER 13
INTELLIGENT
INFORMATION SYSTEMS
Hossein BIDGOLI
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
1
Chapter 13 Intelligent Information Systems
learning outcomes
LO1
Define artificial intelligence and explain how these
technologies support decision making.
LO2
Explain an expert system, its applications, and its
components.
LO3
LO4
Describe case-based reasoning.
LO5
Describe fuzzy logic and its uses.
Summarize types of intelligent agents and how
they’re used.
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
2
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.
Describe how genetic algorithms are used.
Explain natural language processing and its
advantages and disadvantages.
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
3
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
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
4
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
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
5
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:
–
–
–
–
Why?
What does it mean?
What should be done?
When should it be done?
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
6
Table 13.1
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
Applications of AI Technologies
7
Chapter 13 Intelligent Information Systems
Robotics
• Some 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
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
8
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
– Mobility, limited vision, and some speech capabilities
• Robots have some unique advantages in the
workplace compared with humans
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
9
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 heuristics
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
10
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)
• Explanation facility
• Inference engine
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
11
Exhibit 13.1
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
An Expert System Configuration
12
Chapter 13 Intelligent Information Systems
Components of an Expert System (cont’d.)
• Forward chaining
– Series of “If-Then-Else”
– Condition pairs are performed
• “If” condition is evaluated first
• Then the corresponding “Then-Else” action is
carried out
• Backward chaining
– Starts with the goal first—the Then part
– Backtracks to find the right solution
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
13
Chapter 13 Intelligent Information Systems
Components of an Expert System (cont’d.)
• Semantic (associative) networks
– Represent information as links and nodes
• Frames
– Store conditions or objects in hierarchical order
• Scripts
– Describe a sequence of events
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
14
Chapter 13 Intelligent Information Systems
Uses of Expert Systems
•
•
•
•
•
•
•
•
•
Airline industry
Forensics lab work
Banking and finance
Education
Food industry
Personal management
Security
US Government
Agriculture
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
15
Chapter 13 Intelligent Information Systems
Expert Systems in Baltimore County Police
Department
• In Baltimore County, an expert system was developed so
that detectives could analyze information about burglary
sites and identify possible suspects
• Detectives could enter statements about burglaries, such
as neighborhood characteristics, the type of property
stolen, and the type of entry used; they could also get
information on possible suspects
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
16
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
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
17
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
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
18
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 are solved better by human experts
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
19
Chapter 13 Intelligent Information Systems
Advantages of Expert Systems
• Never becomes distracted, forgetful, or tired
• Duplicates and preserves the expertise of scarce
experts
• Preserve the expertise of employees who are
retiring or leaving an organization
• Creates consistency in decision making
• Improves the decision-making skills of
nonexperts
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
20
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 still no match found:
– Human expert must solve the problem
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
21
Chapter 13 Intelligent Information Systems
Intelligent Agents
• Bots (short for robots)
• Applications of artificial intelligence are
becoming more popular
– Particularly in e-commerce
• Consist of software capable of reasoning and
following rule-based processes
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
22
Chapter 13 Intelligent Information Systems
Intelligent Agents (cont’d.)
• Characteristics:
–
–
–
–
–
–
Adaptability
Autonomy
Collaborative behavior
Human-like interface
Mobility
Reactivity
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
23
Chapter 13 Intelligent Information Systems
Intelligent Agents (cont’d.)
• Web marketing
– Collects 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
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
24
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:
–
–
–
–
PriceScan
BestBookBuys.com
www.mysimon.com
DogPile
• Searches the Web by using several search engines
• Eliminates duplicate results
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
25
Chapter 13 Intelligent Information Systems
Personal Agents
• 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
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
26
Chapter 13 Intelligent Information Systems
Data-Mining Agents
• Work with a data warehouse
• Detect trend changes
• Discover new information and relationships
among data items that aren’t readily apparent
• Having this information early enables decision
makers to come up with a solution that
minimizes the negative effects of the problem
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
27
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
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
28
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
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
29
Exhibit 13.3
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
Degree of Membership in a Fuzzy System
30
Chapter 13 Intelligent Information Systems
Uses of Fuzzy Logic
• Used in:
– Search engines, chip design, database management
systems, software development, and more
• Examples:
–
–
–
–
–
Dryers
Refrigerators
Shower systems
TVs
Video camcorders
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
31
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
• Used for poorly structured problems
• Use patterns instead of the “If-Then-Else” rules
that expert systems use
• Create a model based on input and output
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
32
Exhibit 13.4
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
An Artificial Neural Network Configuration
33
Chapter 13 Intelligent Information Systems
Artificial Neural Networks (cont’d.)
• Used for many tasks, including:
–
–
–
–
–
Bankruptcy prediction
Credit rating
Investment analysis
Oil and gas exploration
Target marketing
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
34
Chapter 13 Intelligent Information Systems
Neural Networks in Action
• Many companies are able to predict customers’
shopping behavior based on past purchases
• E-banks use neural networks to rank their
customers into groups
• Visa International
– Introduced a credit authorization system based on
neural networks to reduce credit card fraud
– Could cut fraudulent transactions by as much as 40%
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
35
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
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
36
Chapter 13 Intelligent Information Systems
Natural Language Processing
• Developed so that users can communicate with
computers in their own 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
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
37
Table 13.2
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
NLP Systems
38
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
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
39
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 that the system can respond to inquiries about
the content
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
40
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
• Benefits of integrating an expert system into the
database component of a DSS are:
– Adding deductive reasoning to traditional DBMS
functions
– Improving access speed
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
41
Chapter 13 Intelligent Information Systems
Summary
• Intelligent information systems
– AI technologies are used to support decision-making
processes
• Expert systems
– Components
•
•
•
•
Case-based reasoning
Intelligent agents
Fuzzy logic and genetic algorithms
Natural language processing
MIS, Chapter 13
©2011 Course Technology, a part of Cengage Learning
42