Download Chapter 1 THE INFORMATION AGE IN WHICH YOU LIVE Changing

Chapter 4
DECISION SUPPORT AND ARTIFICIAL
INTELLIGENCE
Brainpower for Your Business
McGraw-Hill/Irwin
Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.
STUDENT LEARNING
OUTCOMES
1. Compare and contrast decision
support systems and geographic
information systems.
2. Define expert systems and describe
the types of problem to which they
are applicable.
3. Define neural networks and fuzzy
logic and the use of these AI tools.
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STUDENT LEARNING
OUTCOMES
1. Define genetic algorithms and list the
concepts on which they are based
and the types of problems they solve.
2. Describe the four types of agentbased technologies.
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VISUALIZING INFORMATION IN MAP
FORM FOR DECISION MAKING
o Geographic information systems
(GISs) allow you to see information
spatially, or in map form.
o Researchers and scientists used a
GIS to map the location of all the
debris from the shuttle Columbia
o The city of Chattanooga uses a GIS to
map the location of its 6,000 trees to
help develop a maintenance schedule
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VISUALIZING INFORMATION IN MAP
FORM FOR DECISION MAKING
o The city of Richmond, VA, used a GIS
to optimize its 2,500 bus stop
locations in its public transportation
system
o Sometimes, a picture is worth a
thousand words
o Recall from Chapter 1, the form of
information often defines its quality
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VISUALIZING INFORMATION IN MAP
FORM FOR DECISION MAKING
1. Do you use Web-based map
services to get directions and
find the location of buildings? If
so, why?
2. In what ways could real estate
agents take advantage of the
features of a GIS?
3. How could GIS software benefit
a bank wanting to determine the
optimal placements for ATMs?
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INTRODUCTION
o Phases of decision making
1. Intelligence – find or recognize a
problem, need, or opportunity
2. Design – consider possible ways of
solving the problem
3. Choice – weigh the merits of each
solution
4. Implementation – carry out the solution
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Four Phases of Decision Making
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Types of Decisions You Face
o Structured decision – processing a certain
information in a specified way so that you
will always get the right answer
o Nonstructured decision – one for which
there may be several “right” answers,
without a sure way to get the right answer
o Recurring decision – happens repeatedly
o Nonrecurring (ad hoc) decision – one you
make infrequently
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Types of Decisions You Face
EASIEST
MOST
DIFFICULT
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CHAPTER ORGANIZATION
1. Decision Support Systems
–
Learning outcome #1
2. Geographic Information Systems
–
Learning outcome #1
3. Expert Systems
–
Learning outcome #2
4. Neural Networks and fuzzy Logic
–
Learning outcome #3
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CHAPTER ORGANIZATION
1. Genetic Algorithms
–
Learning outcome #4
2. Intelligent Agents
–
Learning outcome #5
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DECISION SUPPORT SYSTEMS
o Decision support system (DSS) – a
highly flexible and interactive system
that is designed to support decision
making when the problem is not
structured
o Decision support systems help you
analyze, but you must know how to
solve the problem, and how to use the
results of the analysis
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Alliance between You and a DSS
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Components of a DSS
o Model management component –
consists of both the DSS models and
the model management system
o Data management component –
stores and maintains the information
that you want your DSS to use
o User interface management
component – allows you to
communicate with the DSS
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Components of a DSS
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GEOGRAPHIC
INFORMATION SYSTEMS
o Geographic information system
(GIS) – DSS designed specifically to
analyze spatial information
o Spatial information is any information
in map form
o Businesses use GIS software to
analyze information, generate
business intelligence, and make
decisions
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Zillow GIS Software for Denver
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ARTIFICIAL INTELLIGENCE
o DSSs and GISs support decision making;
you are still completely in charge
o Artificial intelligence, the science of
making machines imitate human thinking
and behavior, can replace human
decision making in some instances
–
–
–
–
Expert systems
Neural networks (and fuzzy logic)
Genetic algorithms
Intelligent agents (or agent-based
technologies)
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EXPERT SYSTEMS
o Expert (knowledge-based) system –
an artificial intelligence system that
applies reasoning capabilities to reach
a conclusion
o Used for
– Diagnostic problems (what’s wrong?)
– Prescriptive problems (what to do?)
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Traffic Light Expert System
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What Expert Systems Can
and Can’t Do
o An expert system can
– Reduce errors
– Improve customer service
– Reduce cost
o An expert system can’t
– Use common sense
– Automate all processes
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NEURAL NETWORKS AND
FUZZY LOGIC
o Neural network (artificial neural
network or ANN) – an artificial
intelligence system that is capable of
finding and differentiating patterns
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Neural Networks Can…
o Learn and adjust to new
circumstances on their own
o Take part in massive parallel
processing
o Function without complete information
o Cope with huge volumes of
information
o Analyze nonlinear relationships
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Fuzzy Logic
o Fuzzy logic – a mathematical method
of handling imprecise or subjective
information
o Used to make ambiguous information
such as “short” usable in computer
systems
o Applications
– Google’s search engine
– Washing machines
– Antilock breaks
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GENETIC ALGORITHMS
o Genetic algorithm – an artificial
intelligence system that mimics the
evolutionary, survival-of-the-fittest
process to generate increasingly
better solutions to a problem
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Evolutionary Principles of
Genetic Algorithms
1. Selection – or survival of the fittest
or giving preference to better
outcomes
2. Crossover – combining portions of
good outcomes to create even better
outcomes
3. Mutation – randomly trying
combinations and evaluating the
success of each
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Genetic Algorithms Can…
o Take thousands or even millions of
possible solutions and combine and
recombine them until it finds the
optimal solution
o Work in environments where no model
of how to find the right solution exists
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INTELLIGENT AGENTS
o Intelligent agent – software that
assists you, or acts on your behalf, in
performing repetitive computer-related
tasks
o Types
– Information agents
– Monitoring-and-surveillance or predictive
agents
– Data-mining agents
– User or personal agents
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Information Agents
o Information Agents – intelligent
agents that search for information of
some kind and bring it back
o Ex: Buyer agent or shopping bot –
an intelligent agent on a Web site that
helps you, the customer, find products
and services you want
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Monitoring-andSurveillance Agents
 Monitoring-and-surveillance
(predictive) agents – intelligent
agents that constantly observe and
report on some entity of interest, a
network, or manufacturing equipment,
for example
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Data-Mining Agents
 Data-mining agent – operates in a
data warehouse discovering
information
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User Agents
o User or personal agent – intelligent
agent that takes action on your behalf
o Examples:
–
–
–
–
–
Prioritize e-mail
Act as gaming partner
Assemble customized news reports
Fill out forms for you
“Discuss” topics with you
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MULTI-AGENT SYSTEMS AND
AGENT-BASED MODELING
o Biomimicry – learning from
ecosystems and adapting their
characteristics to human and
organizational situations
o Used to
1. Learn how people-based systems behave
2. Predict how they will behave under
certain circumstances
3. Improve human systems to make them
more efficient and effective
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Agent-Based Modeling
o Agent-based modeling – a way of
simulating human organizations using
multiple intelligent agents, each of
which follows a set of simple rules and
can adapt to changing conditions
o Multi-agent system – groups of
intelligent agents have the ability to
work independently and to interact
with each other
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Business Applications
o Southwest Airlines – cargo routing
o P&G – supply network optimization
o Air Liquide America – reduce
production and distribution costs
o Merck – distributing anti-AIDS drugs in
Africa
o Ford – balance production costs &
consumer demands
o Edison Chouest – deploy service and
supply vessels
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Swarm Intelligence
 Swarm (collective) intelligence – the
collective behavior of groups of simple
agents that are capable of devising
solutions to problems as they arise,
eventually leading to coherent global
patterns
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Characteristics of Swarm
Intelligence
o Flexibility – adaptable to change
o Robustness – tasks are completed
even if some individuals are removed
o Decentralization – each individual has
a simple job to do
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Assign #5 – Short-Answer
Questions, pg. 155 - #1, 6, 7;
Discussion Questions, pg.
156 - #1.
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