Transcript 13. Intelligent Information Systems.

BIDGOLI
MIS
5
13
INTELLIGENT
INFORMATION
SYSTEMS
Copyright ©2016 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly
accessible website, in whole or in part.
LEARNING OUTCOMES
1 Define artificial intelligence, and explain
how AI technologies support decision
making
2 Describe an expert system, its applications,
and its components
3 Describe case-based reasoning
4 Summarize the types of intelligent agents
and how they are used
5 Describe fuzzy logic and its uses
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LEARNING OUTCOMES (continued)
6 Explain artificial neural networks
7 Describe how genetic algorithms are used
8 Explain natural-language processing and its
advantages and disadvantages
9 Summarize the advantages of integrating AI
technologies into decision support systems
10 Explain contextual computing
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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
• Involve computer application to areas that require
knowledge, perception, reasoning, understanding,
and cognitive abilities
• Concerned with generating and displaying
knowledge and facts
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AI Technologies Supporting Decision Making
• Decision makers use information
technologies in decision-making analyses
of:
• What-is – Analysis used in transactionprocessing systems and management
information systems
• What-if – Analysis used in decision support
systems
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Table 13.1
Applications of AI Technologies
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Robots
• Involve application of AI
• Excel at performing simple, repetitive tasks
• Free workers from tedious or hazardous
jobs
• Have limited mobility
• Operation is controlled by a computer
program that includes commands
• Includes programming languages for
controlling
• Variable Assembly Language (VAL)
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Robots
• Use following programming languages
• Variable Assembly Language (VAL)
• Functional Robotics (FROB)
• A Manufacturing Language (AML)
• Honda’s Advanced Step in Innovative
Mobility (ASIMO)
• Most advanced and most popular robots
• Coordinates with other robots
• Personal robots: Posses limited mobility,
vision, and speech capabilities
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Robotics
• Advantages of robots over humans in the
workplace
• Never fall in love with coworkers, get insulted, or
call in sick
• Are consistent
• Used in environments that are hazardous to
humans
• Risk of spying for competitors, asking for a raise,
or lobbying for longer breaks does not exist
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Expert Systems
• Mimics human expertise in a field to solve a
problem in a well-defined area
• Used for activities that human experts have
already handled successfully
• Tasks in medicine, geology, education, and oil
exploration
• Work with heuristic data which encourages
applying knowledge based on experience to
solve or describe a problem
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Components of an Expert System
• Knowledge acquisition facility
• Software package
• Has manual or automated methods for acquiring
and incorporating new rules and facts so the expert
system is capable of growth
• Knowledge base
• Similar to a database, but in addition to storing
facts and figures it keeps track of rules and
explanations associated with facts
• Includes factual, heuristic, meta types of knowledge
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Components of an Expert System
• Knowledge base management system
(KBMS)
• Similar to a DBMS
• Used to keep the knowledge base updated, with
changes to facts, figures, and rules
• User interface: Provides user-friendly access
to the expert system
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Components of an Expert System
• Explanation facility
• Performs tasks similar to what a human expert
does by explaining to end users how
recommendations are derived
• Inference engine
• Similar to the model base component of a
decision support system
• Uses techniques of forward and backward
chaining to manipulate a series of rules
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Components of an Expert System
- Forward chaining: Series of “if-then-else”
condition pairs is performed
- Backward chaining: Expert system starts with
the goal and backtracks to find the right
solution
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Exhibit
13.1
An Expert System Configuration
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Uses of Expert Systems
Forensics lab
work
Banking and
finance
Education
Food industry
Personnel
management
Security
US
Government
Agriculture
Airline industry
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Criteria for Using Expert Systems
• Experience and knowledge of several
experts is available
• 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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Criteria for Using Expert Systems
• Subject domain is limited
• Decision or task involves many rules and
complex logic
• Involves scarcity of experts in the
organization
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Criteria for Not Using Expert Systems
• Very few rules are involved
• Existence of too many rules that slow down
the processing capability to unacceptable
levels
• Well-structured numerical problems
involved
• Broad range of topics is involved, but there
are not many rules
• Disagreement among experts
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Advantages of Expert Systems
Never become distracted, forgetful, or tired
Duplicate and preserve the expertise of scarce experts
Preserve the expertise of retiring or relieving employees
Create consistency in decision making
Improve the decision-making skills of nonexperts
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Case-Based Reasoning
• Problem-solving technique that matches a
new case with a previously solved case and
its solution, stored in a database
• Offers a solution after searching for a match
• Failing to find a match, human expert is required
to solve the problem
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Intelligent Agents
• Software capable of reasoning and
following rule-based processes
• Popular in e-commerce
• Known as:
• Bots (short for robots)
• Virtual agents (VAs)
• Intelligent virtual agents (IVAs)
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Characteristics of Intelligent Agents
Adaptability
Autonomy
Collaborative behavior
Humanlike interface
Mobility
Reactivity
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Applications of Intelligent Agents
• Web marketing: Collecting following
information about customers
• Items purchased
• Demographic information
• Expressed and implied preferences
• Virtual catalogs
• Display product descriptions based on
customers’ previous experiences and
preferences
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Categories of Available Intelligent Agents
Shopping and
information
Personal
Data-mining
Monitoring
and
surveillance
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Shopping and Information Agents
• Help users navigate through the vast
resources available on the Web
• Provide better results in finding information
• Serve as:
• Search engines
• Site reminders
• Personal surfing assistants
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Personal Agents
• Perform specific tasks for a user
• Remembering information for filling out Web
forms
• Completing e-mail addresses after the first few
characters are typed
• Tasks performed by e-mail personal agent
• Generate auto-response messages
• Forward incoming messages
• Create e-mail replies based on the content of
incoming messages
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Data-Mining Agents
• Work with a data warehouse
• Detect trends and discover information and
relationships among data items that were
not readily apparent
• Helps detect potential problems that may arise
in future which enables to come up with a
solution that minimizes the negative effects of
the problem
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Monitoring and Surveillance Agents
• Track and report on computer equipment
and network systems to predict when a
system crash or failure might occur
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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
Degree of Membership in a Fuzzy System
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Areas Where Fuzzy Logic is Used
•
•
•
•
Search engines
Chip design
Database management systems
Software development
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Artificial Neural Networks (ANNs)
• Networks that learn and are capable of
performing tasks that are difficult with
conventional computers
• Playing chess, recognizing patterns in faces and
objects, and filtering spam e-mail
• Used for poorly structured problems
• Use patterns instead of the if-then-else
rules used by the expert systems
• Create a model based on input and output
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Exhibit 13.4
Artificial Neural Network Configuration
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Tasks Involving the Use of ANNs
Bankruptcy
prediction
Credit rating
Investment
analysis
Oil and gas
exploration
Target marketing
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Genetic Algorithms (GAs)
• Search algorithms that mimic the process of
natural evolution
• Used to generate solutions to optimization
and search problems using mutation,
selection, crossover, and chromosome
techniques
• Designed to find the combination of inputs
that generate the most desirable outputs
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Natural Language Processing (NLP)
• Developed so that users can communicate
with computers in human language
• Provides question-and-answer setting that’s
natural and easier for people to use
• Useful with databases
• Use for:
• Call routing
• Stock and bond trading
• Banking by phone
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Table
13.2
NLP Systems
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Categories in NLP Systems
• 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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Activities Performed by NLP Systems
• 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
• Summarize important points and store
information so the system can respond to
inquiries about the content
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Integrating AI Technologies into Decision
Support Systems
• AI technologies help improve the quality of
decision support systems (DSSs)
• Adds explanation capabilities by integrating
expert systems and learning capabilities by
integrating ANNs
• Creates an interface that is easier to use by
integrating an NLP system
- Systems are called integrated DSSs (IDSSs)
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Contextual Computing
• Computing environment that is always
present and is capable perceive the
surroundings and offer recommendations
based on individual need and requirement
• Based on the principle that computers can
both sense and react to the environments
• Similar to how human brains understand and
interpret stimuli
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Contextual Computing
• Allows for tailoring a course of action to a
user in a situation and environment based
on what it knows about the user
• Achieved by using the information
technologies
•
•
•
•
•
Computer networks
Software
Hardware
Database systems
AI technologies
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KEY TERMS
• Artificial intelligence (AI)
• Artificial neural networks (ANNs)
• Backward chaining
• Case-based reasoning (CBR)
• Contextual computing
• Data-mining agents
• Expert systems
• Explanation facility
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KEY TERMS
• Forward chaining
• Fuzzy logic
• Genetic algorithms (GAs)
• Inference engine
• Intelligent agents
• Knowledge acquisition facility
• Knowledge base
• Knowledge base management system (KBMS)
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KEY TERMS
• Monitoring and surveillance agents
• Natural-language processing (NLP)
• Personal agents
• Robots
• Shopping and information agents
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SUMMARY
• Artificial intelligence technologies apply
computers to areas that require knowledge,
perception, reasoning, understanding, and
cognitive abilities
• Intelligent agents are becoming more
popular, especially in e-commerce
• Fuzzy logic is designed to help computers
simulate vagueness and uncertainty in
common situations
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SUMMARY
• Genetic algorithms examine complex
problems without any assumptions of what
the correct solution should be
• Size and complexity of the human language
has made developing NLP systems difficult
• Contextual computing allows for tailoring a
course of action to a user in a situation and
environment based on its knowledge about
the user
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