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CHAPTER 4
DECISION SUPPORT AND ARTIFICIAL INTELLIGENCE
Brainpower for Your Business
THE CHAPTER IN SHORT FORM…
This chapter introduces your students to the various types of decision support that are available.
Computer-aided decision support has two major categories: systems that help you analyze and those that
make the decision for you. The latter includes the various types of artificial intelligence systems.
The first section after the introduction discusses decision types and the process of decision making. It
includes key terms such as structured decision, nonstructured decision, recurring decision,
nonrecurring decision, intelligence, design, choice, and implementation.
The next three sections discuss three types of decision support that aid in the analysis of information:
Decision support systems, collaboration systems, and geographic information systems. These sections
include key terms such as model management, data management, collaboration system, and
geographic information system.
The following five sections discuss various types of artificial intelligence.
 Artificial intelligence (key term – robot)
 Expert systems (key terms – domain expertise, knowledge engineer, knowledge base, and rulebased system)
 Neural networks (key terms – neural network, self-organizing neural network and backpropagation neural network)
 Genetic algorithms (key terms – genetic algorithm, selection, crossover, and mutation)
 Intelligent agents (key terms – shopping bot, user agent, monitoring-and-surveillance agent,
data-mining agent, autonomy, adaptivity, and sociability)
STUDENT LEARNING OUTCOMES
1.
2.
3.
4.
5.
6.
7.
8.
Define decision support system, list its components, and identify the type of applications it’s suited to.
Define collaboration systems along with their features and uses.
Define geographic information systems and state how they differ from other decision support tools.
Define artificial intelligence and list the different types that are used in business.
Define expert systems and describe the type of problems to which they are applicable.
Define neural networks, their uses, and a major strength and weakness of these AI systems.
Define genetic algorithms and list the concepts on which they are based, and the types of problems
they solve.
Define intelligent agents, list the four types, and identify the types of problems they solve.
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LECTURE OUTLINE
INTRODUCTION (p. 134)
DECISIONS, DECISIONS, DECISIONS (p. 134)
1. How You Make a Decision
2. Types of Decisions You Face
DECISION SUPPORT SYSTEMS (p. 136)
1. Components of a Decision Support System
COLLABORATION SYSTEMS (p. 140)
1. Enterprisewide Collaboration
2. Supply-Chain Collaboration
3. Web-Based Collaboration
GEOGRAPHIC INFORMATION SYSTEMS (p. 143)
ARTIFICIAL INTELLIGENCE (p. 145)
EXPERT SYSTEMS (p. 147)
1. Components of an Expert System
2. What Expert Systems Can and Can’t Do
NEURAL NETWORKS (p. 152)
1. Types of Neural Networks
2. Inside a Neural Network
GENETIC ALGORITHMS (p. 156)
INTELLIGENT AGENTS (p. 158)
1. Buyer Agents
2. User Agents
3. Monitoring-and-Surveillance Agents
4. Data-Mining Agents
5. Components of an Intelligent Agent
END OF CHAPTER (p. 163)
1. Summary: Student Learning Outcomes Revisited
2. Closing Case Study One
3. Closing Case Study Two
4. Key Terms and Concepts
5. Short-Answer Questions
6. Assignments and Exercises
7. Discussion Questions
8. Real HOT Electronic Commerce
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KEY TERMS AND CONCEPTS
KEY TERMS AND CONCEPTS
TEXT PAGE
Adaptive filtering
Adaptivity
Artificial intelligence (AI)
Autonomy
Back-propagation neural network
Buyer agent (shopping bot)
Choice
Collaboration system
Collaborative filtering
Crossover
Data-mining agent
Decision support system (DSS)
Design
Domain expert
Domain expertise
Expert system (knowledge-based system)
Explanation module
Genetic algorithm
Geographic information system (GIS)
Implementation
Inference engine
Intelligence
Intelligent agent
Knowledge acquisition
Knowledge base
Knowledge engineer
Model management
Monitoring-and-surveillance agent (predictive agent)
Mutation
Neural network (artificial neural network, ANN)
Nonrecurring (ad hoc) decision
Nonstructured decision
Profile filtering
Psychographic filtering
Recurring decision
Robot
Rule-based expert system
Selection
Self-organizing neural network
Sociability
Structured decision
User agent (personal agent)
User interface management
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156
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158
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139
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136
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136
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CHAPTER 4
Introduction
OPENING CASE STUDY
Using an Integrated Decision Support System to Help Treat Diabetes and Asthma
The opening case study describes how decision support systems can be used to help diagnose diabetes
and asthma and help in managing and monitoring the treatment. The DSS is integrated with electronic
prescription and with a web-based support to help the patient manage his/her own disease.
Key Points:
 Multi university research project that is under way.
 IT is increasingly used in the health care to help in managing disease and achieve a patient-centred
care, where the patient is highly involved in managing his/her disease.
The case raises the issue of the acceptance by patients of using IT in treating disease.
SUPPORT
Extended Learning Modules
 XLM/C – if your students have little previous exposure to technology, cover this module which
introduces a variety of technology hardware and software terms.
 XLM/D – this module is an introduction to networks and gives your students the basics on the
structure and importance of networks.
Skills Modules (CD-ROM)
 Skills Module 1 - this module covers some of the powerful features of spreadsheets, which help with
information analysis.
Real HOT Group Projects (CD-ROM)
1: Assessing the Value of Information - use spreadsheet software to redefine an auto mechanics shop.
3: Building a Decision Support System – use spreadsheet software to analyze stock portfolio options.
5: Outsourcing Information Technology – use spreadsheet software determine the advisability of
outsourcing a customer service centre.
7: Should I Buy or Should I Lease? – use spreadsheet software to perform a cost benefit analysis on
buying versus leasing a delivery truck.
A: Assessing the Value of Information - use spreadsheet software to determine where to sell homes.
B: Executive Information System Reporting – use spreadsheet software to determine the best
organization of political campaign contributions reports.
Web Support (www.mcgrawhill.ca/college/haag)
 Finding Investment on the Internet
 Learning about Investing
 Researching the Company behind the Stock
 Finding Other Sources of Company Financials
 Making Trades Online
 Retrieving Stock Quotes
 Computer-Aided Decision Support
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Introduction
INTRODUCTION
Decision making is a vital and sometimes complex activity that all businesses engage in daily.
Computer-aided decision making comes in two forms: (1) those that aid the decision maker in
analyzing information, and (2) those that use artificial intelligence techniques to perform some decisionmaking task independently (see Figure 4.1 on page 134.)
Key Points:
 Today, computers can do more than crunch numbers. They can augment and even, to a limited
degree, replace human thinking and reasoning processes.
 There are two major categories of computer-aided decision support: (1) Various types of decision
support systems, and (2) artificial intelligence systems.
 There are three types of decision support systems:
 Individual decision support systems
 Collaboration systems
 Geographic information systems.
 There are four types of artificial intelligence decision support:
 Expert systems
 Neural networks
 Genetic algorithms
 Intelligent agents.
Concept Reinforcement: Adding Value – Class Participation
 It’s helpful at this point to stress the importance of correctly defining the problem.
 Much effort and many resources go into solving the wrong problem.
DECISIONS, DECISIONS, DECISIONS
Decisions range from those for which there is a straightforward way of making the decision to those
that require complex analysis before the decision can be made.
Key Points:
 Decision making requires a lot of resources.
 Decision making can take a lot of time.
How You Make a Decision (p. 135)
Key Points:
 The decision-making process has four distinct phases.
 These are (1) intelligence, (2) design, (3) choice, and (4) implementation (see Figure 4.2 on page
135).
 You will often revisit one or more phases during the decision-making process, particularly if you’re
making a complex decision.
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Decisions, Decisions, Decisions
Key Term: Intelligence – the first step in the decision-making process where you find or recognize a
problem, need, or opportunity (also called the diagnostic phase of decision-making).
Key Term: Design – the second phase of the decision-making process. It’s where you consider
possible ways of solving the problem, filling the need, or taking advantage of the opportunity,
Key Term: Choice – the third phase of the decision-making process where you decide on a plan to
address the problem or opportunity.
Key Term: Implementation – the final step in the decision-making process where you put your plan
into action.
Concept Reinforcement: Adding Value –Class Participation
 Point out to your students that the decision-making process is seldom linear – you often go back
and forth before you find an acceptable solution.
 It’s usually the case that the less structure the problem has, the more you’re likely to agonize over
the decision, especially if the decision is very important.
 Some psychologists believe that we don’t go through the steps of decision-making, as detailed
above, but that we decide what to do based on intuition and our “gut” and then find reasons to
justify the decision.
Types of Decisions You Face (p. 135-136)
Key Points:
 One way to classify decisions is by their degree of structure. A decision can range along a
continuum from very structured to not structured at all (see Figure 4.3 on page 136).
 In reality, most decisions fall in between structured and nonstructured, having elements of both.
 A second way to classify decisions is according to how often you make them.
 A recurring decision happens repeatedly, while a nonrecurring one happens infrequently.
Key Term: Structured decision – processing a certain kind of information in a specified way so that
you will always get the right answer.
Key Term: Nonstructured decision – a decision for which there may be several “right” answers, and
there is no precise way to get a right answer.
Key Term: Recurring decision – a decision that happens repeatedly, and often periodically, whether
weekly, monthly, quarterly, or yearly.
Key Term: Nonrecurring or ad hoc decision – a decision that you make infrequently (perhaps only
once), and you may even have different criteria for determining the best solution each time.
Concept Reinforcement: Real HOT Group Projects #1, #A, and #B (CD-ROM)
 These projects require students to use spreadsheet software to analyze information to make a
decision.
 Any one of them serves as an example of the coming sections on decision support.
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Decisions, Decisions, Decisions

The projects are designed to take some time to complete, so assigning them at the beginning of
the chapter will help give your students a concrete example of the concepts that they learn about
in this chapter.
DECISION SUPPORT SYSTEMS
In this section you’ll discuss decision support systems (DSS) in the narrowest sense. A DSS combines
the knowledge worker’s experience, intuition, judgment, and knowledge with the power of information
technology (see Figure 4.4 on page 137).
Here, you’ll show your students examples and discuss the components of a DSS in general.
Key Term: Decision support system (DSS) – a highly flexible and interactive IT system that is
designed to support decision making when the problem is not structured.
Concept Reinforcement: Adding Value – Class Participation
 Be sure to emphasize the “not structured” part of the definition of a decision support system.
 It means that DSSs are suitable for nonstructured problems and those that fall in between
structured and nonstructured.
Components of a Decision Support System (p. 137-140)
Key Points:
 The first part of this section is an example of how Lands’ End uses a decision support system to
plan product lines and inventory levels.
 The components of a decision support system are the (1) model management component, (2) data
management component, and (3) user interface management component.
 Figure 4.5 on page 138 shows how these components work together.
Key Term: Model management - consists of both the DSS models and the DSS model management
system.
Key Term: Data management - performs the function of storing and maintaining the information that
you want your DSS to use.
Key Term: User interface management - allows you to communicate with the DSS.
Concept Reinforcement: Real HOT Group Projects #3, #5, #7, and #B (CD-ROM)
 These projects involve decision support in the form of analysis of options
 They all use spreadsheets to analyze different sets of data under different circumstances.
 If your students are not familiar with the more sophisticated features of spreadsheets, such as
pivot tables, and so on, Extended Learning Module D would be a good module to cover.
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Decision Support Systems
COLLABORATION SYSTEMS
In today’s business world, teams are very important. IT can help teams in various ways. Figure 4.6 on
page 141 shows an example of people physically dispersed around the world, but working together in a
virtual workplace.
E-mail is the simplest example of a collaboration system, but it can be so much more.
Key Term: Collaboration system – software that is designed specifically to improve the performance
of teams by supporting the sharing and flow of information.
Enterprisewide Collaboration (p. 140-141)
Key Points:
 Many companies start out with e-mail and then expand their collaboration systems to include
many other features.
 Some of these features include tele- video- and Web-conferencing, as well as project management
and work flow automation.
Concept Reinforcement: Adding Value – Class Participation
 Ask students whether they have worked on projects in a team and what kind of problems they
had.
 Ask them whether those collaborative tasks are structured or nonstructured.
 Emphasize that in today’s business world, it’s becoming increasingly important to be able to work
with others, and even work with others whom you have never met and may never meet.
Supply-Chain Collaboration (p. 141-142)
Key Points:
 Supply-chain management means considering and working with all your suppliers and their
suppliers in the planning, production, and distribution of raw materials and finished goods.
 Companies as diverse as Boeing and Ogilvy & Mather, who manage advertising for IBM, Ford, and
others, use collaboration systems.
 There’s lots more about supply chain management in Chapter 2.
Concept Reinforcement: Extended Learning Module D – Network Basics
 In order to facilitate supply chain, enterprise-wide, or Web-based collaboration systems, you need
networks.
 If your students are not familiar with the basics of networks, this would be a good module to
cover.
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Collaboration Systems
Web-Based Collaboration (p. 142)
Key Points:
 Web-based collaboration covers a wide range of functions.
 Web-based collaboration is very important in e-commerce, as your students will see in Chapter 5.
 Essentially it means sharing of information using the Internet.
 Several examples are given in the text, and there’s another in the Global Perspective box below.
Concept Reinforcement: Global Perspective – Collaboration to Find Cures (p. 143)
 GlaxoSmithKline, a British pharmaceutical company, has 4,000 researchers and spends $4 billion
a year in research and development.
 Information within the company is stored in more than a dozen databases.
 The researchers also need information that’s on the Web.
 The company set up a collaboration system to make is easier for researchers to find what they
need. This saves time resulting in shortened development time.
Peer-to-Peer Collaboration (p. 142)
Key Points:
 Peer-to-peer collaboration is based on the Napster idea – facilitating the transfer of files from one
Internet user to another.
 Groove and NextPage are examples of systems that provide file sharing capabilities for
businesses.
 An example of how this works is detailed in the text. The example shows how a shipping company
and its customers can stay in contact and make adjustments during the course of the delivery
process to their mutual advantage.
Concept Reinforcement: Adding Value - Class Participation
 The Groove site at www.groove.net/solutions/scenarios has many other examples of how peer-topeer collaboration systems can work.
 NextPage at www.nextpage.com has other examples.
GEOGRAPHIC INFORMATION SYSTEMS
Any information that can be expressed in map form can be part of a geographic information system,
which is a combination of textual and spatial information into business geography.
Key Points:
 A geographic information system (GIS) represents information thematically with overlapping layers
(see Figure 4.7 on page 144).
 Each layer shows a different type of information.
 A GIS is a combination of database and graphics technology.
 GIS information consists of information from many databases.
 Examples of GISs are included in this section in the text.
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Geographic Information Systems
Key Term: Geographic information system (GIS) – is a decision support system designed
specifically to work with spatial information.
Concept Reinforcement: Adding Value – Class Participation
 This would be a good place to discuss information representation and the human cognitive
process.
 Psychology research says that a person can comfortably handle 7±2 chunks of information at any
one time. This is called the “rule of 7.”
 Graphs and maps are particularly useful for presenting information because they pack so much
information into one chunk. For example, you can see the distribution of fire hydrants on a city
map easier than you can decipher the addresses of the hydrants from a narrative or table.
 A geographic information system combines graphic and map information achieving great
economy of information chunks.
ARTIFICIAL INTELLIGENCE
Artificial intelligence (AI) replaces human thinking, but only in a very narrow, restricted sense. AI
systems mimic one narrowly defined human thought process, such as recognizing certain types of
patterns, or choosing from a predictable set of choices.
Key Points:
 AI systems are fundamentally different from decision support systems in that they reach a
conclusion by themselves and are not dependent on the user knowing how to solve the problem.
 Institutions as diverse as hospitals, the IRS, and the armed forces use one or more types of
artificial intelligence systems.
 Four types of artificial intelligence systems used in business are discussed in this chapter.
 They are:
 Expert systems, which reason through problems and offer advice in the form of a conclusion or
recommendation
 Neural networks, which learn to differentiate patterns
 Genetic algorithms, which provide increasingly better solutions to problems through multiple
iterations.
 Intelligent agents, which work independently, carrying out repetitive and predicable tasks,
adapting along the way.
Key Term: Artificial intelligence – the science of making machines imitate human thinking and
behaviour.
Key Term: Robot – a mechanical device equipped with simulated human senses and the capability of
taking action on its own.
Concept Reinforcement: Industry Perspective – Robots to the Rescue: Big Ones and Baby
Ones (p. 146)
 When the Twin Towers went down, lots of people were trapped in the rubble.
 To help the rescue workers a new kind of robot was used – a marsupial type of robot that carries
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Artificial Intelligence


lots of very small robots to the evacuation scene and sends them tethered into the rubble to find
victims.
In the future, the small robots will be wirelessly controlled so that no tether is necessary.
Robots are used other places too, such as in the Pentagon for mail delivery and in the food
service industry.
EXPERT SYSTEMS
An expert system is an artificial intelligence system that asks questions, and based on the answers,
asks more questions, continuing the process until it can reach a conclusion or make a
recommendation, or, until it determines that it hasn’t got enough information or rules to reach a
conclusion.
Components of an Expert System (p. 149-151)
Key Points:
 Expert systems are usually built to fill a specific need or suit a particular situation.
 Expert systems come in two types: (1) those that are diagnostic and answer the question “What’s
wrong?” and (2) those that are prescriptive in nature and answer the question “What to do?”
 An example is given in the text explaining an expert system in terms of dealing with traffic lights
while driving.
 Figure 4.9 on page 149 has an example of the rules and the user interface screen of the traffic
lights expert system.
 Figure 4.10 on page 150 shows all the components of an expert system in relation to each other.
 An expert system has three kinds of components and three types of each of these kinds of
components:
 Information types
 Domain expertise
 “Why?” information
 Problem facts
 People
 Domain expert
 Knowledge engineer
 Knowledge worker
 IT components
 Knowledge base
 Knowledge acquisition
 Inference engine
 User interface
 Explanation module
Key Term: Expert system or knowledge-based system – an artificial intelligence system that applies
reasoning capabilities to reach a conclusion.
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Expert Systems
Key Term: Domain expertise – the set of problem-solving steps – the reasoning process that will solve
the problem.
Key Term: Domain expert – provides the domain expertise in the form of problem-solving strategies.
Key Term: Knowledge engineer – the person who formulates the domain expertise into an expert
system.
Key Term: Rule-based expert system – the type of expert system that expresses the problem-solving
process as rules.
Key Term: Knowledge base – stores the rules of the expert system.
Key Term: Knowledge acquisition – the components of the expert system that the knowledge
engineer uses to enter the rules.
Key Term: Inference engine – the part of the expert system that takes your problem facts and
searches the knowledge base for rules that fit.
Key Term: Explanation module – the part of an expert system where the “why” information, supplied
by the domain expert, is stored to be accessed by knowledge workers who want to know why the
expert system asked a question or reached a conclusion.
Concept Reinforcement: Industry Perspective – Please Send All Applications to the Expert
System (p. 148)
 Some companies that hire a lot of people and have high employee turnover use expert systems to
try and get the best employees they can from the candidate pool.
 For example, when you apply for a job at Target, you answer a list of questions (some of which
are shown in the Industry Perspective box in the text).
 Based on the applicant’s answers the expert system assigns the candidate a rating of yes, no, or
maybe.
 From there a human resource person takes the application further or not, depending on the
recommendation of the expert system.
What Expert Systems Can and Can’t Do (p. 151-152)
Key Points:
 Expert systems capture human expertise.
 As such they can handle more information, more consistently, with less errors than people can.
 However, some human expertise is difficult or impossible to capture.
 Expert systems can’t expand (or “learn”) beyond the precise situations that the rules dictate.
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Expert Systems
Concept Reinforcement: Team Work – Traffic Lights Revisited (p. 151)
 This team work project would work well as an individual project too. For students who are
inexperienced in programming, a team approach might work better since programming logic is
often difficult for non-programmers.
 Several solutions are possible to this project. For example, pedestrians and dogs could be
handled separately or (as in this solution) grouped together as “live obstacles” that will shortly be
gone; wrecks and stalled vehicles could be handled separately or together.
.
Step
Symptom or fact
Yes
No
Explanation
1.
2.
Go to 2
Go to 3
Go to 9
Go to 4
Might be obstacles in way
Might be able to get around
Go around
Go to 7
STOP
Go to 1
STOP
Go to 1
Go to 5
Check light again if delayed
4.
Is the light green?
Is there a wreck or stalled car
blocking the way?
Is there a clear path around the
blockage?
Are people or animals in the way?
5.
6.
Has a ball rolled out?
Are children following the ball?
7.
Are you turning left?
Go to 6
STOP
Go to 1
Go to 8
8.
Is traffic approaching?
9.
Is the light red?
STOP
Go to 1
Go to 10
10.
Are you turning right?
Go to 11
11.
Is traffic approaching from left?
12.
Is the light likely to change?
STOP
Go to 1
Go to 13
13.
Can you stop?
STOP
14.
Is traffic approaching?
Prepare
Crash
3.
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People and animals will
move
Check light again if delayed
Go to 7 Might be children around
Go to 7 Have to wait for children
Check light again if delayed
GO ON Can only turn left when
clear
GO ON Light might turn red
Check light again if delayed
Go to Should stop, may not be
12
able to, or may be turning
right
Go to Check for cross traffic
12
GO ON Must wait for clear path
Check light again if delayed
Go to Will only reach this point if
14
light is yellow
Go to Should stop, may be a
14
problem if you can’t
to GO ON Unless the intersection is
clear, you’re likely to crash
CHAPTER 4
Neural Networks
NEURAL NETWORKS
Neural networks are used in situations where pattern recognition or differentiation is required. They’re
based on the structure and pattern-recognition function of the human brain.
Key Points:
 Neural networks are useful for identification, classification, and prediction when a vast amount of
information is available.
 They are used for tasks as diverse as finding trace elements of explosives in airport luggage and
finding diseased tissue in biopsies.
 Many more examples of the application of neural networks are given in the text.
Key Term: Neural network or artificial neural network or ANN – an artificial intelligence system that
is capable of finding and differentiating patterns.
Types of Neural Networks (p. 154)
Key Points:
 There are two basic types of neural networks: (1) self-organizing neural networks and (2) backpropagation neural networks.
Key Term: Self-organizing neural network – finds patterns and relationships in vast amounts of data
by itself.
Key Term: Back-propagation neural network – a neural network trained by someone.
Concept Reinforcement: Industry Perspective – Caution! The Neural Network Is Watching You
(p. 153)
 Since neural networks are good at recognizing patterns they are used widely for detecting fraud
by looking at an overall pattern of behaviour or cues in financial information.
 This example discusses how neural networks spot fraudulent use of convenience checks that
banks often send their customers to use instead of, or in conjunction with, credit cards.
 The neural network looks for certain characteristics of the transaction to determine its legitimacy.
Inside a Neural Network (p. 154-155)
Key Points:
 Neural networks have three layers: (1) the input layer, (2) the hidden or middle layer, and (3) the
output layer (see Figure 4.11 on page 155).
 Neural networks can learn to differentiate patterns by adjusting the weights in the middle layer
 In contrast to an expert system, a neural network can “learn.”
 Historically, the biggest problem with neural networks has been that even the programmers weren’t
really sure what was going on in the hidden layer and thus couldn’t tell precisely how the neural
network worked. Newer neural networks allow you to manually adjust the weights, thereby giving
the programmer more flexibility and control.
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Neural Networks
Concept Reinforcement: Team Work – How Would You Classify People? (p. 154)
 This assignment is intended to get students to think about appropriate and inappropriate uses of
information technology in general and neural networks in particular.
 If you have a small class you could divide students into two groups and get one side to argue for
and the other against such use of a neural network.
 If your class is large, you could divide the students into many groups with one spokesperson per
group.
 Making lists on the board or an overhead slide of the arguments pro and anti gives a good
overview of what the class thinks about the issue.
GENETIC ALGORITHMS
Genetic algorithms are based on the natural process of evolution and are used when an optimal
solution is needed in the face of an almost infinite number of inputs and results.
Key Points:
 Genetic algorithms use three concepts of evolution: (1) selection, (2) crossover, and (3) mutation.
 In the text, an example of putting together a portfolio of stocks demonstrates the type of problem
for which a genetic algorithm is admirably suited.
 Many examples of applications of genetic algorithms are also in this section in the text.
Key Term: Genetic algorithm – an artificial intelligence system that mimics the evolutionary, survivalof-the-fittest process to generate increasingly better solutions to a problem.
Key Term: Selection – gives preference to better outcomes.
Key Term: Crossover – portions of good outcomes are combined in the hope of creating an even
better outcome.
Key Term: Mutation – the process of trying combinations and evaluating the success (or failure) of the
outcome.
Concept Reinforcement: Industry Perspective – The Evolution of Farming Equipment (p. 157)
 Deere, a company that manufactures farming equipment, tries to be as flexible as possible in
meeting its customers’ desires for features on its tractors.
 Since there are many possible combinations of features, Deere uses a genetic algorithm to
schedule the manufacturing process for the greatest output with the least cost.
Concept Reinforcement: On Your Own – Be a Genetic Algorithm and Put Nails in Boxes
(p.158)
 This project can, of course, be completed in ways other than the “trial-and-error” method shown
here.
 Constraints were
 3 types of nails per box
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Genetic Algorithms
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 weight not to exceed 20 ounces
 no more than 30 nails per box
 at least 5 and not more than 10 nails per box
Requirement is to find the highest profit possible
For the first trial take as many of the most profitable nails as possible (10 of the 4inch; 10 of the
3.5 inch, and 10 of the 3 inch). This gives a profit of $85, but the total weight is 25.5 ounces,
which is too heavy.
So, one strategy emerges, which would be to keep 10 of the 4 inch and 10 of the 3.5 inch and try
the other sizes.
For the second trial, take 10 of the 4 inch; 10 of the 3.5 inch, and 6 of the 2 inch (since 5 of the 3
inch makes the box too heavy as does more than 6 of the 2 inch). This results in a weight of 20
ounces and a profit of $67.25. So we’ll see if we can do better.
The third trial takes 10 of the 4 inch; 10 of the 3.5 inch and 10 of the 1.5 inch nails. This box
weighs 19.5 ounces and yields a profit of $70.
As it turns out, the highest profit possible is $70, and there are several combinations that will
work.
INTELLIGENT AGENTS
The term “Intelligent agent” is the umbrella term for many types of automated AI systems that perform
repetitive and predicable tasks within computer systems.
Key Points:
 A primitive example of an intelligent agent is the animated paper clip in Word that offers to help
with letters and so on.
 There are four main categories of intelligent agents
 Buyer agents or shopping bots
 User or personal agents
 Monitoring-and-surveillance or predictive agents
 Data-mining agents
Key Term: Intelligent agent – software that assists you, or acts on your behalf, in performing repetitive
computer-related tasks.
Buyer Agents (p. 159-160)
Key Points:
 Buyer agents or shopping bots travel around a network (usually the Internet) retrieving information.
 MySimon is the most well-known example of a buyer agent (see Figure 4.12 on page 159).
 Buyer agents are also used by sites to make offers to surfers. They use many techniques to
predict the preferences of consumers.
 Four types of prediction systems are mentioned in the text. These are:
 Collaborative filtering
 Profile filtering
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CHAPTER 4
Intelligent Agents


Psychographic filtering
Adaptive filtering.
Key Term: Buyer agent or shopping bot – an intelligent agent on a Web site that helps you find
products and services you want.
Key Term: Collaborative filtering – a method of placing you in an affinity group of people with the
same characteristics.
Key Term: Profile filtering – requires that you choose terms or enter keywords to provide a more
personal picture of you and your preferences.
Key Term: Psychographic filtering – anticipates your preferences based on the answers you give to a
questionnaire.
Key Term: Adaptive filtering – asks you to rate products or situations and also monitors your actions
over time to find out what you like and dislike.
Concept Reinforcement: On Your Own – Go Bargain Hunting Online (p. 161)
 If you have a classroom with computers available, this project, or a scaled-down version, could
be completed during class time.
 Students should get a lot of hits for each of the items.
 There will likely be great variation on the “look and feel” of the sites and in the information offered
about pricing.
User Agents (p. 160-161)
Key Points:
 User agents come in many forms.
 Their tasks are many and varied and range from playing computer games with you to filling out
forms on the Web and “discussing” the topics of your choice with you.
 User agents are beginning to be used on Intranets and extranets for negotiating and making deals
on routine products and services and to automate other business processes.
Key Term: User agent or personal agent – an intelligent agent that takes action on your behalf.
Key Term: Intranet – an internal organizational Internet that is guarded against outside access by a
special security feature called a firewall (which can be software, hardware, or a combination of the
two).
Key Term: Extranet – an intranet that is restricted to an organization and certain outsiders, such as
customers and suppliers.
Monitoring-and-Surveillance Agents (p. 161-162)
Key Points:
 Monitoring-and-surveillance agents are also called predictive agents because they warn of possible
trouble before it happens.
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CHAPTER 4
Intelligent Agents


These intelligent agents are used to monitor equipment, specifically large networks, to spot
bottlenecks and failures before they bring the network down.
Monitoring-and-surveillance agents are not restricted to monitoring equipment. They can also
monitor Internet sites for deals or changes, for example, or help you keep track of the competition.
Key Term: Monitoring-and-surveillance agent or predictive agent – intelligent agent that observes
and reports on equipment.
Concept Reinforcement: Adding Class Value
 If you want to show your class an actual intelligent agent, you can find one at Carnegie Melon
University at www-2.cs.cmu.edu/~softagents/textminer/goodnews_process.htm.
 This one, called Good News is a Text Miner, classifies electronic news articles..
Data-Mining Agents (p. 162-163)
Key Points:
 One of the main objectives of data mining is discovery of new information, and that requires a lot of
searching and retrieving, which is what intelligent agents do best.
 A common type of data mining is classification, or finding patterns in information and categorizing
information into classes.
 Since neural networks are designed specifically for pattern recognition and classification, datamining agents often incorporate neural network technology.
Key Term: Data-mining agent – operates in a data warehouse discovering information.
Concept Reinforcement: Adding Value – Class Participation
 If you haven’t covered Chapter 3, this would be a good place to briefly discuss the uses of a
database.
 A data warehouse is composed of many operational databases and requires a different type of
query tool in addition to the traditional ones.
 Intelligent agents, specifically data-mining agents, are one such tool and may be composed of
several different types of artificial intelligence.
Concept Reinforcement: Global Perspective – Data Mining in the Body Shop (p. 162)
 The Body Shop sends out a lot of catalogues to its customers.
 With data mining agents the company was able to identify those customers who were most likely
to actually buy from the catalogue.
 Data mining also helped with finding Web customers who would buy from the catalogue.
 With data mining, The Body Shop increased its revenue per catalogue by 20%
Components of an Intelligent Agent (p. 163)
Key Points:
 The word “intelligent“ implies that intelligent agents have intelligence or the capacity to learn.
 At the moment the “intelligence” part is still in its early stages.
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CHAPTER 4
Intelligent Agents

To be truly intelligent an agent would have to have three characteristics: (1) autonomy, (2)
adaptivity, (3) sociability
Key Term: Autonomy – the ability of an intelligent agent to act without your telling it every step to take.
Key Term: Adaptivity – discovering, learning, and taking action independently
Key Term: Sociability – the ability of intelligent agents to confer with each other.
Concept Reinforcement: Adding Value – Class Participation
 There is lots of information on intelligent agents on the Web.
 For example, Stanford University has an article that discusses the features of intelligent agents
and what differentiates them from other software.
 The address is www-cdr.stanford.edu/NextLink/Expert.html.
 To gain a better understanding of the features, advantages, and disadvantages of intelligent
agents, ask your students to do some research on the topic.
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CHAPTER 4
Summary: Student Learning Outcomes Revisited
SUMMARY: STUDENT LEARNING OUTCOMES REVISITED
In each chapter and module, we revisit the student learning outcomes as a mechanism and format for
summarizing the chapter.
You’ll find this content for Chapter 4 on page 163-164.
Following the adage “Tell them what you’re going to tell them, tell them, and then tell them what you
told them,” you should walk through the summary with your students.
You should also inform your students that the summary is great support for studying for exams.
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CHAPTER 4
Closing Case Study One
CLOSING CASE STUDY ONE
Using Neural Networks to Categorize People (p. 164-166)
This case study illustrates, on a personal level, how some businesses are using AI techniques to find and
take care of their most profitable customers. The businesses profiled include a bank, a credit card
company, a supermarket and a movie studio. Since students are customers of all these types of
businesses, perhaps even of the companies mentioned here, they will have little trouble relating to the
issues.
QUESTIONS
1.
A neural network learns to recognize patterns based on past information. One set of people is judged
by the behaviour of another. Is this fair or reliable? How accurate is it for a business to predict the
future behaviour of customers on the basis of historic information? Don’t people change? Have you
ever changed your behaviour in the course of your life?
DISCUSSION
 People are often creatures of habit, and past behaviour is often a good predictor of future
behaviour.
 However, this generalization is a long way from absolute. In fact, there are also generalizations
about how people change. For example, the adage that says “if you’re not a liberal at 20 you
have no heart, but if you’re still a liberal at 40 you have no brains.” The implication here is that
people become more conservative with age. Again, the truth is that some do and some don’t.
 Given these contradicting doctrines (that people don’t change and that people do change) all
predictions, even those that sophisticated computer software makes, must be treated with
extreme caution.
 Predictions of human behaviour can only reasonably be made within very narrow contexts and
with very careful control of variables.
 This is a good topic for getting a discussion started.
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CHAPTER 4
Closing Case Study One
2.
Customers are not likely ever to see the information that companies are using to pigeon-hole them.
Even the company executives may not know what criteria the neural network uses. How important
are the assumptions underlying the software (i.e., the facts that the neural network are given about
customers)? Even the IT specialists who design neural networks can’t vouch for their accuracy or
specify exactly how the neural network reaches its conclusions. Is this safe for businesses? What are
the possible business consequences of using neural networks without assurances of their reliability?
DISCUSSION
 The underlying assumptions are not just important, they are crucial. All results and predictions
flow from the assumptions that are made initially.
 A neural network chooses certain criteria as being the most influential in deciding what group a
particular person or example belongs in. So the neural network depends on getting the correct
inputs (the characteristics or assumptions) to find the most influential characteristics.
 The company using a neural network is not likely to be able to explain how it works, and perhaps
the company that sold the neural network can’t either. So, the buyer/user will be in some difficulty
if asked to defend a particular decision in court. For example, if someone claimed discrimination
after being turned down for a loan or a credit card.
 The consequences that a company might face are lawsuits, complaints to the Better Business
Bureau, bad press, loss of customers, and so on.
3.
Businesses can use segmenting to suggest products and services to you, or if you request it, to
prevent your getting junk mail you don’t want. Is that good? Would receiving wanted information or
avoiding junk mail be worth the price of being categorized?
DISCUSSION
 As with almost everything, there are good and bad aspects of companies having and being able
to manipulate large amounts of information on customers.
 The customer benefits because he/she may get offers that are appealing or may be protected
from solicitations that are unappealing.
 How you feel about this is really a very personal matter.
4.
Say you run a business that supplies medical equipment (not prescription drugs) — wheelchairs,
hospital beds, heating packs. You’re trying to determine which customers you should give
preferential treatment to. What assumptions or variables would you use (for example, age, income,
and so on) to segment your customer population?
DISCUSSION
 After the Health Insurance Portability and Accountability Act goes into effect, you won’t be able to
get patient information from the health care industry very easily.
 A law passed in 1996 called gave the health care industry two years to implement policies and
procedures to keep patient information confidential. It hasn’t yet been completely implemented at
the time of writing.
 You might be able to get information from group care homes, insurance companies, retirement
communities, and associations that cater to people with disabilities. You would need age,
income, disability type, degree of disability, insurance coverage, physical size, and so on.
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CHAPTER 4
Closing Case Study One
5.
Do you think that this segmentation practice is fair? First, consider the business stockholders, then
consider the customers. Does it matter whether it’s fair or not? Why or why not? Should there be
laws against it, or laws controlling it, or none at all? Explain and justify your answer.
DISCUSSION
 This is likely to generate a lot of opinions, and answers may vary greatly.
 You could segment the class into a stockholder group and a customer group, and ask each
group to advance arguments.
 Also ask the students if they have any examples of a company they don’t do business with
having information on them. For example, did they ever get call from a loan company offering
them a way to consolidate their credit card debt, or from a phone company that seemed to know
how much long distance calling they do.
6.
Does the practice make business sense? If you owned stock in a company, how would you feel
about this practice? Do you think you should get better treatment if you’re a better customer? Do you
think people who are not such good customers should get the same deal that you get? Would it make
any difference whether the company collected the information and did the neural network analysis
itself, or bought the information or the whole package from a third party?
DISCUSSION
 The practice may make good business sense since the employees’ efforts can be concentrated
where they will yield the greatest profit.
 Many people think that they should get better treatment as a frequent customer than a casual
one-timer.
 Some students might feel better if the company that did the collection and analysis were the
same company that they do business with. It may feel more personal to them.
 Many others won’t think it makes any difference.
7.
Is this the same as redlining, or is it OK because it looks at behaviour and classifies people rather
than assuming characteristics based on membership in a particular group?
DISCUSSION
 Many of your students may feel that considering an individual’s characteristics is more fair than
assuming them from membership in a particular group.
 This method at least analyzes each customer individually.
 However, in the long run it may not make much real difference.
 Either way, the company is still predicting future behaviour based on past behaviour, which may
or may not be a valid basis for prediction.
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CHAPTER 4
Closing Case Study Two
CLOSING CASE STUDY TWO
Decision Support and Artificial Intelligence in Healthcare (p. 166-167)
In this closing case study, your students will read about the use of artificial intelligence in the healthcare
industry and how two different kinds of hospitals used AI to provide better patient care.
In St-John’s, the issue was the effectiveness of using hand-held computers for home visits over a wireless
network. Even though everyone seemed pleased with the new system, can you identify some drawbacks
to the system? At Sunnybrook, it is patient-record software being examined. In both instances, there are
similar goals: track symptoms, treatment and outcomes and ultimately providing better service to patients.
QUESTIONS
1.
The two cases had essentially the same goals - to track symptoms, treatment, and outcomes - that
require the collection and maintenance of a huge amount of qualitative and quantitative information.
What type of software would you recommend for storing this information so that it can be easily
accessed and analyzed? What sort of software query tools would you suggest?
DISCUSSION
 A database and/or a data warehouse would be the most obvious answer.
 Along with the data repository, you’d need query and analysis tools such as you use with
databases, and also multidimensional analysis tools for data warehouses.
 On a practical level, students might suggest Access or Excel, both of which have analysis
features.
2.
Would there be a role for a geographic information system help in either of these examples? How
could it be useful? Are there extreme cases, perhaps natural disasters, where a GIS would be
useful?
DISCUSSION
 A GIS system could be used to identify many patterns among patients.
 For example, for cancer research you would need to investigate people who all have the same
type of cancer and ask questions such as whether their demographic location is a contributing
factor.
 A GIS could be used to plan new hospital building projects.
 A GIS could identify places where counseling and other help is needed for issues such as
educating teens on pregnancy and sun exposure and older people on obesity and heart disease.
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CHAPTER 4
Closing Case Study Two
3.
How could a neural network help the Newfoundland Nurses to generate the information it needs?
Would it be advisable to automate the medication process for routine illnesses by connecting the
pattern recognition abilities of a neural network to a robot that dispenses pills and letting medical staff
deal with more complicated cases? Why? Why not?
DISCUSSION
 There will likely be divided opinions on this question.
 Some possible points of difference could include possible misdiagnosis, botched prescriptions, or
even possible overdoses.
 Even though technology is wonderful, it cannot substitute for the human touch.
 People also tend to view technology with deep suspicion when it takes the place of a trusted
professional, like a doctor.
4.
An expert system is designed to ask questions, and then ask more questions based on the answers
to the previous questions. Isn’t that what medical specialists do when they’re diagnosing your illness?
Would you, therefore, like to dispense with visiting a doctor and just buy a medical expert system that
you could install on your home computer and consult when you don’t feel well? Why? Why not?
DISCUSSION
 Students’ views on this will probably vary, but in general, we have found that people tend not to
want to substitute a computer for the personal touch.
 As a variation, you could have your student’s do some research on the recent problems caused
by patients looking on the Internet for medical advises. There have been many cases of patients
misdiagnosing themselves.
 On the other hand, many patients have used this information to become more aware of their
existing illnesses.
5.
What sort of collaboration might be helpful for the Newfoundland Nurses and the doctors at
Sunnybrook? What situations can you envisage where a video or a Web conference would be
helpful?
DISCUSSION
 Any number of possibilities for collaboration systems exist in health care.
 Some examples include conferences on medical issues, discussions on diagnosing difficult
patients, guest speakers, and possibly using video casting to help doctors view unique surgeries
(such as those of separating conjoined twins).
 There is application of such systems for developing countries so that specialists can help out
without actually having to travel around the world.
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CHAPTER 4
Closing Case Study Two
6.
For both cases, describe tasks that the various types of artificial intelligence software would lend
themselves to and state specifically what tasks each AI system is suited to.
DISCUSSION
 Your students may come up with many examples.
 Decision support systems could be used for planning and analysis of health issues.
 Neural networks could be used to detect patterns in illnesses and for drug interactions.
 Intelligent agents could keep track of the hospitals’ networks to spot possible troubles, and to
facilitate research.
 Geographic information systems could be used to analyze the population of the surrounding
district who will be served by the hospitals.
7.
Part of quality assurance in any organization is the identification and correction of things that went
wrong and ways in which processes can be improved. It’s no different in health care – errors are
inevitable. However, it’s part of the mission of health care workers to keep these errors as small and
as infrequent as possible. How could a decision support system help in the implementation of safer
procedures? How part could expert systems, neural networks, and intelligent agents play?
DISCUSSION
 A decision support system could be used to analyze what medications should be kept in the
hospital and how much to have on hand of each medication.
 Expert systems could be used to help with a personality profile of employees.
 A neural network could be used to find patterns of mistakes and errors to try and predict what
conditions make them more likely.
 Intelligent agents could check sequence of procedures and find relevant research on medical
procedures.
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CHAPTER 4
Short-Answer Questions
SHORT-ANSWER QUESTIONS (p. 168)
1.
What are the four types of decisions discussed in this chapter? Give an example of each.
ANSWER: Nonrecurring, or ad hoc, decision is one that you make infrequently (perhaps only
once) and you may even have different criteria for determining the best solution each time. A merger
with another company is an example. Recurring decisions are decisions that you have to make
repeatedly and often periodically, whether weekly, monthly, quarterly, or yearly. An example would be
which route to take to go to work. Nonstructured decision is a decision for which there may be
several “right” answers and there is no precise way to get a right answer. An example would be
whether to change a company’s strategy. Structured decision is a decision where processing a
certain kind of information in a specified way so that you will always get the right answer. An example
would be deciding how much to pay employees. p. 136
2.
What is a DSS? Describe its components.
ANSWER: A decision support system (DSS) is a highly flexible and interactive IT system that is
designed to support decision making when the problem is not structured. The components of a
decision support system are the model management component, the data management component,
and the user interface management component. p. 136
3.
What sort of a system would you use if you wanted to work with your suppliers electronically?
ANSWER: You would most likely use a collaboration system. p. 140
4.
What are three of the features that collaboration systems might have?
ANSWER: Three features of collaboration systems are Web-conferencing, project management, and
work flow automation p. 140
5.
What is a geographic information system used for?
ANSWER: A geographic information system is used for any type of information that can be
represented spatially. p. 143
6.
How is information represented in a geographic information system?
ANSWER: A geographic information system represents information thematically – in overlapping
layers. p. 145
7.
What is artificial intelligence? Name the artificial intelligence systems used widely in business.
ANSWER: Artificial intelligence (AI) is the science of making machines imitate human thinking and
behaviour. The types of artificial intelligence widely used in business are expert systems, neural
networks, genetic algorithms, and intelligent agents. p. 145
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CHAPTER 4
Short-Answer Questions
8.
What are the components of an expert system?
ANSWER: There are three sets of components: Information, people, and IT components. Information
types are domain expertise, “Why” information, and the facts of the particular situation. The people
involved are the domain expert, the knowledge engineer, and the knowledge workers. The IT
components in an expert system are the knowledge base, the knowledge acquisition component, the
Inference engine, the user interface, and the explanation module. p. 147
9.
What three concepts of evolution are used by the genetic algorithm?
ANSWER: The three concepts are selection, mutation, and crossover. Selection is the feature of a
genetic algorithm that give preference to better outcomes. Mutation is a feature of a genetic
algorithm; it’s the process of trying combinations and evaluating the success (or failure) of the
outcome. Crossover is the feature of a genetic algorithm where portions of good outcomes are
combined in the hope of creating an even better outcome. p. 156
10. What are intelligent agents? What tasks can they perform?
ANSWER: Intelligent agents are software that assists you, or act on your behalf, in performing
repetitive computer-related tasks. They can find good deals on the Internet, monitor computer
networks for failures, fill out forms, play computer games, and so on. p. 158
11. What do shopping bots do?
ANSWER: A buyer agent or shopping bot is an intelligent agent on a Web site that helps you, the
customer, find the products and services you want. p. 159
12. What do monitoring-and-surveillance agents do?
ANSWER: Monitoring-and-surveillance agents (or predictive agents) are intelligent agents that
observe and report on equipment. p. 161
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CHAPTER 4
Short-Question Answers
SHORT-QUESTION ANSWERS
Although they are not included in the book, these Question-Answers can be used by the instructor as an
help to review the concepts covered in the chapter.
1.
A decision that you face every day.
QUESTION: What is a recurring decision? p. 136
2.
The part of a DSS where the models are stored.
QUESTION: What is the model management component? p. 139
3.
Any system that simulates human senses and can act on its own.
QUESTION: What is an artificial intelligence system? p. 145
4.
A domain expert must provide the rules for this AI system.
QUESTION: What is a rule-based expert system? p. 150
5.
The person who enters the rules into the expert system.
QUESTION: What is the knowledge engineer? p. 150
6.
Classification is the strong suit of this AI system.
QUESTION: What is a neural network? p. 152
7.
This AI system takes its cue from evolution.
QUESTION: What is a genetic algorithm? p. 156
8.
It recognizes patterns and classifies input.
QUESTION: What is a neural network? p. 152
9.
This type of software usually has e-mail, instant messaging, and calendar management.
QUESTION: What is a collaboration system? p. 140
10.
It can find the best price for a product on the Internet.
QUESTION: What is a buyer agent or a shopping bot? p. 159
11.
Adaptivity and sociability are two of its qualities.
QUESTION: What is a truly intelligent intelligent agent? p. 158
12.
It shows information in map form.
QUESTION: What is a geographic information system? p. 143
13.
Crossover and mutation are features of how this software works.
QUESTION: What is a genetic algorithm? p. 156
14.
It’s the second stage of decision making.
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CHAPTER 4
Short-Question Answers
QUESTION: What is design? p. 135
15.
It’s the name for the process that slices and dices the information in a data warehouse.
QUESTION: What is a data-mining agent? p. 162
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CHAPTER 4
Assignments and Exercises
ASSIGNMENTS AND EXERCISES (p. 168-170)
1.
Collaboration Work. In a group of two or more students, collaborate on a project to make a list of
100 videos or music CDs. Classify the videos or CDs into groups. For example, if you choose
movies, your categories might be adventure, comedy, classic, horror, musicals, among others. All
communication about the project must be electronically communicated (but not by phone). You could
use e-mail, set up a Web site, use a chat room, or use a collaboration e-room, if your university has
that facility. Print out a copy of all correspondence on the project and put the correspondence
together in a folder in chronological order.
Was this task very different from collaborating face-to-face with your partners? In what ways was it
better, in what ways worse? What additional problems or advantages would you expect if the person
or people you’re working with were in a different hemisphere?
DISCUSSION
 Students will usually talk about advantages like not having to find meeting times that suit
everyone and not having to remember and travel to meeting places.
 The disadvantages will most likely include lack of personal interaction and misunderstandings,
along with a longer time required to resolve conflicts.
 If partners are in a different hemisphere, the time difference will probably loom large. Then there
may be language and cultural difficulties.
2.
Choose a Financing Option. Using a spreadsheet (like Excel, for example) evaluate your options
for a $12,000 car. Compare the payments (use the =pmt function in Excel), the total amount of
interest, and the total you’ll pay for the car under the following four options:
A. 3 years at 0% interest
B. 2 years at 1.99% annual percent rate (APR)
C. 4 years at 5% APR
D. 6 years at 6% APR
What other considerations would you take into account if you’re going to buy a new car? Are there
considerations other than the interest rate and the other parts that can be calculated? What are they?
How is a car different from other purchases, such as CDs or TV sets or computers?
DISCUSSION
 The numerical results are as follows:
 3 Years @ 0%;
Payments=$333.33
Total Interest = $0.00
Total for the Car=$12,000

2 Years @ 1.99%;
Payments = $510.43
Total Interest = $250.33
Total for the Car = $12,250.33

4 Years @ 5%;
Payments = $276.35
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CHAPTER 4
Assignments and Exercises
Total Interest = $1264.87
Total for the Car=$13,264.87




3.
6 Years @ 6%;
Payments=$198.87
Total Interest = $14318.98
Total for the Car=$2318.98
There are many considerations involved in buying a new car.
The interest rate is only one factor. It may not be as important as keeping payments down so
that a person might choose to pay for longer with a higher interest rate.
A car is different from CDs, TV, and computers in that it usually costs more and will require
payments for much longer. A car doesn’t usually lose it value as fast as the other items. Buying a
car usually requires a lot more thought and effort than small purchases like CDs.
Which Software Would You Use? Which type or types of computer-aided decision support
software would you use for each of the situations in the following tasks? Note why you think each of
your choices is appropriate. The decision support alternatives are:
 Decision support system
 Collaboration system
 Geographic information system
 Expert system
 Genetic algorithm
 Intelligent agent
Problem
You and another marketing executive on a different continent
want to develop a new pricing structure for products
You want to predict when customers are about to take their
business elsewhere
You want to fill out a short tax form
You want to determine the fastest route for package delivery to
23 different addresses in a city
Type of Decision Support
Collaboration system
Expert system or
neural network
User agent
Genetic algorithm or
geographic information
system
You want to decide where to spend advertising dollars (TV, radio, Decision support system
newspaper, direct mail, e-mail)
You want to keep track of competitors’ prices for comparable Monitoring-andgoods and services
surveillance agent
DISCUSSION
 Marketing executives on two continents: Collaboration system so that they can share ideas and
documents
 Predicting customer behaviour: Neural networks could be used to spot the pattern of transactions
and frequency that indicate coming withdrawal. Expert systems could be used to check the
answers to questions with the conclusion of “likely to leave” or “not likely to leave.”
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CHAPTER 4
Assignments and Exercises


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4.
Tax forms: A user agent that fills out forms, with special features for tax forms.
Fastest route: A genetic algorithm can try all possible routes and pick the best one. A geographic
information could help analyze the routes.
Advertising dollars: A decision support system can analyze the options based on performance in
the past and other factors.
Competitors’ prices: Monitoring-and-surveillance agents can keep track of the Web sites of
competitors and report back on significant information.
Find Some Neural Networks. Go to the Internet and find three neural network software packages.
Make a short report on the three you find. Be sure to include in your report the following:
 The applications (finance, manufacturing, accounting, fraud detection, etc.)
 Whether the product is free or, if not, how much it costs (if you can determine this)
 The operating system required to run the software
 Any special hardware or software requirements listed
Do any of those sites explain how the neural network works? Did you have to register to get
information?
DISCUSSION
 What are the differences between a data warehouse and a database?
 Why has OLTP become so popular? Why is it necessary to the functioning of some functions
within organizations?
5.
What Should the Music Store Owner Do? A music store owner wants to have enough of the
hottest CDs in stock so that people who come in to buy a particular CD won’t be disappointed – and
the store won’t lose the profit. CDs that are not sold within a certain length of time go onto the sale
table where they may have to be sold at cost, if they sell at all. She wants to design a decision
support system to try and predict how many copies she should purchase and what information she
will need.
List some of the considerations that would go into such a system. Here is a couple to start you off: (1)
the population of the target market, (2) sales for particular types of music in similar markets.
DISCUSSION
 There are many factors that could be part of the analysis
 Some examples are:
 Distributors, their stock and prices
 Shelf space available
 Cost of advertising
 Staffing to watch the stock and check out customers
 Past sales
 Information on employment and salary levels of customer base
 Predictions about future economic conditions
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Discussion Questions
DISCUSSION QUESTIONS (p. 170-171)
1. Some experts claim that if a business gets 52 percent of its decisions right, it will be successful. Would
using a decision support system guarantee better results? Why or why not? What does the quality of
any decision depend on? Do you think it matters what type of decisions are included in this 52%? For
example, would getting the right type of paper clips be as influential a decision as deciding where to
locate the business? Can you think of a situation where the type of paper clip matters a great deal?
DISCUSSION
 A decision support system will by no means guarantee better results.
 The most important part of decision making with a decision support system is the intuition,
judgment, and experience that the decision maker brings to the process.
 The quality of a decision depends on having the right information, at the right time, in the right
form, analyzing it appropriately, and interpreting the results correctly.
2.
Early system researchers called expert systems “experts in a box.” Today, in most situations, people
who consult expert systems use them as assistants in specific tasks and not to totally replace human
experts. What sorts of tasks would you feel comfortable about having expert systems accomplish
without much human intervention? What sorts of tasks would you not be comfortable having expert
systems handle independently? Give examples. The first famous expert system, called MYCIN, was
developed to diagnose blood diseases. Would you be comfortable consulting an expert system
instead of a live doctor when you’re ill? Why or why not? If you knew that expert system “doctors”
diagnose illness correctly five times as often as human doctors, would this change your answer?
Why or why not?
DISCUSSION
 Why haven’t Expert Systems totally replaced humans?
 Are there situations other than health where an Expert System would be useful?
3.
Consider the topic of data warehouses in Chapter 3. In the future, AI systems will be increasingly
applied to data warehouse processing. Which AI systems do you think might be helpful? For which
tasks, or situations, might they best be applied? Do you think that AI systems will someday play a
greater role in the design of databases and data warehouses? Why or why not?
DISCUSSION
 Intelligent agents are already being used as data mining tools, since their strength lies in
searching through data warehouses to find and retrieve information.
 A neural network is well suited to the analysis of data warehouse information for tasks of
“information discovery” by discerning patterns.
 A genetic algorithm could be applied to a data warehouse in situations that called for many
combinations of characteristics.
4.
Consider the differences and similarities among the four AI techniques discussed in this chapter.
Name some problems that might be amenable to more than one type of AI system. Say you sell
baseballs from your Web site. What types of AI systems could you use to generate information that
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CHAPTER 4
Discussion Questions
would be useful to you in deciding what direction to take your company in the future? If you were
pretty successful at selling baseballs, would you expect to have the amount of information on
customers that, say Wal-Mart, has? Why or why not?
DISCUSSION
 An expert system applies rules to reach a conclusion.
 A neural network finds patterns, a genetic algorithm generates many generations of solutions,
and an intelligent agent finds and retrieves information and also performs repetitive administrative
tasks.
 Diagnosing illness is a problem that has been aided by both expert systems and neural networks.
 Choosing stock portfolios is a task that has been tackled by expert systems, neural networks, and
genetic algorithms.
5.
AI systems are relatively new approaches to solving business problems. What are the difficulties with
new IT approaches in general? For each of the systems we discussed, identify some advantages and
disadvantages of AI systems over traditional business processes. Say you were selling specialty teas
and had brick and click stores. Would you use the same type of AI systems for each part of your
business? In what way would you use them or why would you not? Is there a place for decision
support and artificial intelligence techniques in small specialty businesses? In what way would
decision support add value? Can you think of how a DSS or an AI system would be value reducing (in
terms of Porter’s value chain theory)? What do you see as the major differences between running a
mammoth concern and a small specialty business?
DISCUSSION
 New IT approaches generally present several problems.
 For example, using a new approach often presents implementation and compatibility problems.
 It takes time for a new approach to be understood and accepted.
 People have to be trained to use the new system.
 Collecting and organizing information may be more complicated (as when moving from
databases to a data warehouse).
 New hardware is often necessary for new IT solutions.
 For analysis of customer information you might use the same systems, for example you could
use an expert system on both types of stores to help customers find a tea to suit their tastes.
 But you would probably use different IT tools for the parts of the business that are different. For
example, you could use neural networks to analyze Web traffic to and from your Web site.
 Small businesses have the same types of problems and opportunities as large ones – it’s mainly
a question of degree.
 Decision support could add value by helping to determine customer tastes and preferences.
 Any kind of computer-aided decision tool could be value reducing if it’s not used properly, in
which case the results would be erroneous.
6.
Neural networks recognize and categorize patterns. If someone were to have a neural network that
could scan information on all aspects of your life, where would that neural network potentially be able
to find information about you? Consider confidential (doctor’s office) as well as publicly available
(department of motor vehicles) information.
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CHAPTER 4
Discussion Questions
DISCUSSION
 The list is extremely long. Chapter 8 has a section on privacy that might help in exploring this
topic. Here are some examples:
 Social Security Administration
 Outpatient/emergency departments
at hospitals
 Motor Vehicles Department
 Red Cross
 Real estate agent
 College admissions office
 Travel agent
 Employer
 General practitioner
 IRS
7.
What type of AI systems could your school use to help with registration? Intelligent agents find vast
amounts of information very quickly. Neural networks can classify patterns instantaneously. What sort
of information might your school administration be able to generate using these (or other AI systems)
with all of its student information?
DISCUSSION
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8.
Registration: An expert system would help with deciding whether a student is eligible to take a
certain course. It could ask questions about prerequisites, grade point average, number of credits
passed, and so on.
Additional reports: There are many possible answers to this question. Below are a few.
 Neural networks could look for patterns of students avoiding or seeking out particular courses
or instructors and chart their progress up to and after the course to look for any common
characteristics.
 A neural network could identify critical success factors or students at risk.
 An intelligent agent could go looking on the Web for any chat room discussions, or other
activity of a particular student. It could look in commercial or government databases for
information on students.
Would you be comfortable with your institution allowing a third party (like a credit card company)
access to student information? Would it make a difference if there were no identifying information
included—if only aggregate or summary information were available to the third party? If this third
party got student information (names and all), what sort of AI techniques could it use to generate
support for decision making? What sort of third-party companies (aside from credit card companies)
would like to be able to get student lists from lots of higher education institutions? Identify ten types of
businesses that might be interested in such lists. If these third parties were to get personal
information on you, would you like there to be restrictions on what they can and can’t do with it? If so,
what restrictions would you like to see?
DISCUSSION
 Students may once again bring up the issues of privacy and access to information.
 What is a third party? What if money is at issue here?
 What is the difference between AI and Expert Systems? Have students provide examples
of each.
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CHAPTER 4
Real HOT Electronic Commerce
REAL HOT ELECTRONIC COMMERCE
Finding Investment Opportunities on the Internet
This is a good project to use to get students acquainted with using the Web for investment decisions.
We recommend that you give groups of students a particular industry to research. Alternatively, you could
let students pick their own stocks and have a two- or four-week competition to see whose hypothetical
portfolio performs the best.
In the end, how you structure this assignment will depend on how knowledgeable your students are about
finance in general and investing in particular.
Learning about Investing
 The strongest emphasis should be on learning about investing.
 This could make a huge difference to students’ lives in the long run.
Researching the Company behind the Stock
 This research might turn up some very interesting information about the companies your students
are researching.
 You could ask your students to comment on any reasons they would not invest in a stock, perhaps
because the business the company is in is objectionable to them.
Finding Other Sources of Company Financials
 Financial history is usually considered to be very important in predicting the future success of a
company.
 Students will probably find many sources on their own.
Making Trades Online
 The questions in the text on this topic are necessarily about procedure and don’t require students
to actually part with money.
 Your students will most likely be surprised at the requirements that stock-trading Web sites impose,
like an initial deposit of thousands of dollars.
Retrieving Stock Quotes
 This is a must for anyone who owns or wants to own stock.
 Some sites will even keep your portfolio (real or hypothetical) for you and show you the price of
your chosen stocks. This is a good exercise for people wanting to learning about investing.
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