Download Chapter 1 THE INFORMATION AGE IN WHICH YOU LIVE …

Chapter 4
Decision Support and Artificial Intelligence
Brainpower for Your Business
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
Chapter 4 Slide 1
Making decisions by visualizing
information as maps
A
geographic information system (GIS) allows you
to see information spatially in the form of a map.
 The Ice and Marine Services Branch of the
Meteorological Service of Canada provides accurate
and timely reports on sea ice floes in Canadian
waters. The IMSB depends on integrated GIS and
other information technologies to acquire and
process data from data sources such as satellites,
airborne radars and ice/weather models.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
Chapter 4 Slide 2
Making decisions by visualizing
information as maps
1.
2.
3.
Do you use Web-based map services to
get directions and find the location of
buildings? If so, why?
In what ways could real estate agents take
advantage of the features of a GIS?
How could GIS software benefit a bank
wanting to determine the optimal
placements for ATMs?
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
Chapter 4 Slide 3
Making decisions by visualizing
information as maps
1.
2.
3.
4.
5.
Remember the 4P’s
Product
Price
Promotion
Place
1.
The where of things
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 4
How decisions are made
One model includes these four phases of decision
making:
1.
2.
3.
4.
Intelligence – find or recognize a problem, need, or
opportunity
Design – consider possible ways of solving the
problem
Choice – weigh the merits and consequence of
each solution and then choose one
Implementation – carry out the solution
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
Chapter 4 Slide 5
How decisions are made
Another model called satisficing is simply making a
choice even though it may not be the best one.
Can be called the “just do it” model
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
Chapter 4 Slide 6
Decision making may not be linear.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
Chapter 4 Slide 7
Decision making may not be linear.
http://www.witiger.com/powerpoints/going~international/sld009.htm
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
Chapter 4 Slide 8
Types of Decisions
A
structured decision uses certain inputs and
processes them in a precise way guaranteeing a
correct answer e.g. knowing how much GST to
charge on a bill.
 A nonstructured decision involves intuition. No
rules or criteria exist guaranteeing choice of the right
answer e.g. introduction of a new product line.
 A recurring decision happens repeatedly.
 A nonrecurring (ad hoc) decision is made
infrequently.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
Chapter 4 Slide 9
Types of Decisions
A
structured decision
 Example – what is the cost of materials
 A nonstructured decision
 Example – will the government continue to
subsidize the program
 A recurring decision
 Using a particular shipping partner
 A nonrecurring (ad hoc) decision
 Caterer for the company’s 10th anniversary
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
Chapter 4 Slide 10
Decision Support Systems
 Decision
support system (DSS)
 – a highly flexible and interactive system that is
designed to support decision making for a nonstructured problem
 Decision makers are provided with specialized
support using IT. They must know what information
they need. They must also know how to use the
results of the analysis done by the DSS.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
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Chapter 4 Slide 11
Decision Support Systems
 Decision
support system (DSS)
 Typical information that a decision support
application might gather and present would be:
 Comparative sales figures between one week and
the next
 Projected revenue figures based on new product
sales assumptions
 The consequences of different decision alternatives,
given past experience in a context that is described

Eg. Selling 4 for the price of 3, bundling different
services
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
Chapter 4 Slide 12
The decision maker’s
alliance with the DSS
Page 98
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Chapter 4 Slide 13
Three components of a
Decision Support System
1.
2.
The model management system stores and
maintains the DSS models.
Models represent events, facts or situations.
Businesses use models to represent variables and
the relationships between them.

For example, a bank could use a model to see what
impact various increases to the interest rate would
have on their customers’ mortgage payments.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 14
Components of a Decision Support Systems
2.
The data management component is both the DSS
database management system and information from
the organization
external sources and
users.
3.
The user interface management component consists of the
user interface. This component is where the user inputs
information, commands and models into the DSS.
Page 100
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Chapter 4 Slide 15
Example of how the three DSS
components work together
A user communicates needs to the DSS using the
user interface management component . For
example the user could specify which models to use.
Use of the models is provided by the model
management component of the DSS. The input for
the chosen model(s) is retrieved using the data
management component.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 16
Components of a DSS
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 17
GEOGRAPHIC INFORMATION
SYSTEMS
A
geographic information system (GIS) is a DSS
designed specifically to analyze spatial information.
This spatial information can be shown on a map.
 Businesses use GIS software to analyze
information, generate business intelligence, and
make decisions.
 Business geography refers to the use of GIS
software to generate maps showing something of
interest to the company e.g. maps showing the
location of homes for sale.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 18
GEOGRAPHIC INFORMATION SYSTEMS
 GPS
technology is greatly facilitating the ability of
GIS to provide helpful info
http://www.witiger.com/ecommerce/mcommerceGPS.htm
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 19
Artificial intelligence (AI)
Artificial intelligence is the use of machines to
imitate the way humans think and behave. For
example, an insurance company could use AI to
detect fraudulent claims.
There are four major categories of AI.
1.
2.
3.
4.
expert systems
Page 104
neural networks and fuzzy logic
genetic algorithms
intelligent agents or agent-based technologies
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Chapter 4 Slide 20
Artificial intelligence (AI)
Prof. John McCarthy , Professor of Computer Science
at Stanford University
http://www-formal.stanford.edu/jmc/whatisai/node2.html
Prof. McCarthy (retired) was a famous
Computing Science professor at Stanford
University and he was responsible for the
coining of the term "Artificial Intelligence"
http://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist)
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 21
Artificial intelligence (AI)
Artificial intelligence types according to Prof.
John McCarthy , Professor of Computer Science
at Stanford University
Logical AI
The program decides what to do by inferring that certain actions are
appropriate for achieving its goals
Search
examine large numbers of possibilities, e.g. moves in a chess game
Pattern recognition
For example, a vision program may try to match a pattern of eyes and
a nose in a scene in order to find a face.
Representation
Facts about the world have to be represented in some way. Usually
languages of mathematical logic are used
Inference
For example, when we hear of a bird, we man infer that it can fly
http://www-formal.stanford.edu/jmc/whatisai/node2.html
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 22
Artificial intelligence (AI)
Artificial intelligence types according to Prof. John McCarthy ,
Professor of Computer Science at Stanford University
http://www-formal.stanford.edu/jmc/whatisai/node2.html
Common sense knowledge and reasoning
This is the area in which AI is farthest from human-level
Learning from experience
The approaches to AI based on connectionism and neural nets
specialize in that
Planning
Planning programs start with general facts about the world (especially
facts about the effects of actions)
Epistemology
This is a study of the kinds of knowledge that are required for solving
problems in the world.
Ontology
Ontology is the study of the kinds of things that exist. In AI, the
programs and sentences deal with various kinds of objects
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 23
Artificial intelligence (AI)
Artificial intelligence types according to Prof. John McCarthy ,
Professor of Computer Science at Stanford University
http://www-formal.stanford.edu/jmc/whatisai/node2.html
Heuristics
A heuristic is a way of trying to discover something or an idea
imbedded in a program
refers to experience-based techniques for problem solving
a heuristic process may include running tests and getting results by
trial and error. As more sample data is tested, it becomes easier to
create an efficient algorithm to process similar types of data
Genetic programming
a technique for getting programs to solve a task by selecting the fittest
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 24
Artificial intelligence (AI)
Applications of A.I. according to Prof. John McCarthy ,
Professor of Computer Science at Stanford University
game playing
You can buy machines that can play master level chess for a few hundred
dollars. There is some AI in them, but they play well against people mainly
through brute force computation--looking at hundreds of thousands of
positions. To beat a world champion by brute force and known reliable
heuristics requires being able to look at 200 million positions per second.
speech recognition
In the 1990s, computer speech recognition reached a practical level for
limited purposes. Thus United Airlines has replaced its keyboard tree for
flight information by a system using speech recognition of flight numbers
and city names. It is quite convenient.
On the other hand, while it is possible to instruct some computers using
speech, most users have gone back to the keyboard and the mouse as
http://www-formal.stanford.edu/jmc/whatisai/node3.html
still more convenient.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
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Chapter 4 Slide 25
Artificial intelligence (AI)
Applications of A.I. according to Prof. John McCarthy ,
Professor of Computer Science at Stanford University
understanding natural language
Just getting a sequence of words into a computer is not enough.
Parsing sentences is not enough either.
The computer has to be provided with an understanding of the domain the
text is about, and this is presently possible only for very limited domains.
computer vision
The world is composed of three-dimensional objects, but the inputs to the
human eye and computers' TV cameras are two dimensional.
Some useful programs can work solely in two dimensions, but full computer
vision requires partial three-dimensional information that is not just a set of
two-dimensional views.
At present there are only limited ways of representing three-dimensional
information directly, and they are not as good as what humans evidently
http://www-formal.stanford.edu/jmc/whatisai/node3.html
use.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
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Chapter 4 Slide 26
Artificial intelligence (AI)
Applications of A.I. according to Prof. John McCarthy ,
Professor of Computer Science at Stanford University
expert systems
A ``knowledge engineer'' interviews experts in a certain domain and tries to
embody their knowledge in a computer program for carrying out some task.
One of the first expert systems was MYCIN in 1974, which diagnosed
bacterial infections of the blood and suggested treatments.
It did better than medical students or practicing doctors, provided its
limitations were observed.
heuristic classification
An example is advising whether to accept a proposed credit card purchase.
Information is available about the owner of the credit card, his record of
payment and also about the item he is buying and about the establishment
from which he is buying it (e.g., about whether there have been previous
credit card frauds at this establishment)
http://www-formal.stanford.edu/jmc/whatisai/node3.html
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 27
Types of A.I.
A.I.- Expert Systems
An expert (knowledge-based) system is an artificial
intelligence system that captures expertise in a certain
domain and then applies reasoning capabilities so
that a conclusion can be reached.
For example, an expert system could be used to
diagnose a medical problem. The system could then
recommend a treatment for the condition. The expert
system is useful because previously medical
specialists provided facts and symptoms that were
input into the expert system.
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Chapter 4 Slide 28
Types of A.I.
Traffic Light Expert System
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Chapter 4 Slide 29
Types of A.I.
Expert systems can…
 Handle
massive amounts of information
 Reduce errors
 Combine information from many sources
 Improve customer service
 Provide consistency in decision making
 Provide new information
 Reduce time personnel spend on tasks
 Reduce cost
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Chapter 4 Slide 30
Types of A.I.
Expert systems cannot…
 Capture
expertise if domain experts are unable to
explain how they know what they know
 Be used for reasoning processes that are too
complex
vague
imprecise or
require too many rules
 Use common sense
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Chapter 4 Slide 31
Types of A.I.
Neural Networks and Fuzzy Logic
A neural network (artificial
neural network or ANN) is an
artificial intelligence system
that is capable of finding and
differentiating patterns.
For example, bomb detection
systems in Canadian airport
use neural networks to sense
trace elements in the air.
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Chapter 4 Slide 32
Types of A.I.
Neural Networks Can…
 Learn
and adjust to new circumstances
on their own
 Participate in massive parallel processing
 Function without complete or wellstructured information
 Cope with huge volumes of information
with many dependent variables
 Analyze nonlinear relationships
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 33
Types of A.I.
Neural Networks
 Known
as the “bottom-up” approach to the research
and development of intelligent machines, the neural
network approach seeks to replicate in a computer
the actions and functions of biological neurons found
in the human body.
 Neurons are cellular transmitters of information that
work by means of the electrical signals that pass
through one neuron to another.
 A neural network is, therefore, a group of neurons
that are connected to each other in complex
structures.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 34
Types of A.I.
Neural Networks – fo real? 
 Two
issues are largely responsible for hindering fullscale development of artificial neural networks.
 Firstly, the construction of neuron simulators is costprohibitive.
 Secondly, current computer architecture still needs
more pathways between components.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 35
Types of A.I.
Fuzzy Logic
 Fuzzy
logic is a mathematical method of handling
imprecise or subjective information. It assign values
between 0 and 1 to vague or ambiguous
information. Rules and processes, called algorithms
are constructed. These fuzzy logic algorithms
describe the interdependence among variables.
 For example, fuzzy logic is used by Google’s search
engine to make sense of the search criteria that was
entered.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 36
Types of A.I.
Fuzzy Logic
 handling
imprecise or subjective information

http://www.iscid.org/encyclopedia/Fuzzy_Logic
Original 33 slides by Prof. Anita Beecroft, Kwantlen
Polytechnic University
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16 slides added Feb 2011 by Prof. Tim Richardson, University of Toronto
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Chapter 4 Slide 37
Types of A.I.
Fuzzy Logic
 Fuzzy
logic expands traditional Boolean or
classical logic in order to allow for partial
truths.
 Classical logic requires that a concept be
deemed either true or false, yes or no, black
or white … no allowances for the possibility
that the answer may lie somewhere in the
middle.
 Fuzzy logic, on the other hand, is a superset
that has been developed to manage the
gray areas.
http://www.iscid.org/encyclopedia/Fuzzy_Logic
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 38
Types of A.I.
Fuzzy Logic
 Applications
 Fuzzy
logic normally follows the “if/then”
rules of action and reaction.
 For example, if a temperature reaches the
desired setting, then the thermostat
switches itself off.
 Basic applications of fuzzy logic can be
found in a growing number of household
appliances such as air conditioners,
refrigerators, washing machines, security
systems, etc.
http://www.iscid.org/encyclopedia/Fuzzy_Logic
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 39
Types of A.I.
Genetic Algorithms
A genetic algorithm is an
artificial intelligence system
that tries to find the
combination of inputs that
will produce the best
solution.
Genetic algorithms use
1. selection (preference given to better outcomes)
2. crossover (portions of good outcomes are combined in the hope of creating
an even better outcome)
3. mutation (randomly try new combinations evaluating each combination)
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Chapter 4 Slide 40
Types of A.I.
Genetic Algorithms Can…
 Take
thousands or even millions of possible
solutions, combine and recombine them until it finds
the optimal solution
 Work in environments even if there is no existing
model for finding the right solution
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Chapter 4 Slide 41
Types of A.I.
Intelligent Agents
agent – software that assists you, or
acts on your behalf, when performing repetitive,
computer-related tasks
 There are four types of intelligent agents:
 Intelligent
Information agents
 Monitoring-and-surveillance agents
 Data-mining agents
 User or personal agents
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Chapter 4 Slide 42
Types of A.I.
Information Agents
Information agents are intelligent agents that search
for information of some kind and return it to the user.
An example is a buyer agent or shopping bot which
can help a customer find products or services. When
purchasing a book on Amazon.com, a shopping bot
displays a list of similar books the customer may be
tempted to buy.
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Chapter 4 Slide 43
Types of A.I. – Types of Intelligent Agents
Monitoring-and-Surveillance Agents
Monitoring-and-surveillance (predictive) agents
constantly observe and report things of interest.
For a computer network, a monitoring-andsurveillance agent could be used to look for patterns
of activity and identify potential problems. Agents
could also be used to watch certain Internet sites
looking for stock manipulation or insider training.
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Chapter 4 Slide 44
Types of A.I. – Types of Intelligent Agents
Data-Mining Agents
A data-mining agent is used to discover information
in a data warehouse. It must sift through a lot of
information.
A common data-mining agent looks for patterns in
information and categorizes items into classes. For
example, a data-mining agent could be used to find
investment opportunities in financial markets.
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Chapter 4 Slide 45
Types of A.I. – Types of Intelligent Agents
User Agents
User or personal agents are intelligent agents that
take action on your behalf.
For example, a personal agent could assemble
customized news reports to send you. Another
example is Movex software which searches the
Internet negotiating and making deals with suppliers
and distributors.
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Chapter 4 Slide 46
Types of A.I. – Types of Intelligent Agents
Multi-agent Systems and
Agent-based Modelling
By observing parts of the ecosystem, artificial
intelligence scientists use hardware and software
models to adapt the ecosystem’s characteristics to
human and organizational situations. This is called
biomimicry.
For example, biomimicry could be used to predict how
people will behave under certain circumstances.
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Chapter 4 Slide 47
Types of A.I. – Types of Intelligent Agents
Agent-Based Modelling
modelling – a way of simulating
human organizations using many intelligent agents,
each of which follows simple rules and adapts to
changing conditions
 Multi-agent system – groups of intelligent agents
that can to work independently or interact with each
other
 Air Canada uses agent-based modelling to find the
optimal route to send air cargo.
 Agent-based
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Chapter 4 Slide 48
Types of A.I. – Types of Intelligent Agents
Swarm Intelligence



When individuals in a system consistently follow a
set of rules, complex collective behaviour may
result.
Swarm (collective) intelligence is the collective
behavior of groups of simple agents that can
devise solutions to problems as they come up and
eventually develop a coherent global pattern.
Swarm intelligence can create and maintain
systems that are flexible, robust, decentralized and
self-organized.
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Original 33 slides by Prof. Anita Beecroft, Kwantlen Polytechnic University
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Chapter 4 Slide 49