Chapter 4
Decision Support and Artificial
Intelligence: Brainpower for Your
Business
McGraw-Hill/Irwin
Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved.
STUDENT LEARNING OUTCOMES
1.
2.
3.
Compare and contrast decision support
systems and geographic information
systems.
Define expert systems and describe the
types of problem to which they are
applicable.
Define neural networks and fuzzy logic and
the use of these AI tools.
4-2
STUDENT LEARNING OUTCOMES
4.
5.
Define genetic algorithms and list the
concepts on which they are based and the
types of problems they solve.
Describe the four types of agent-based
technologies.
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AN NFL TEAM NEEDS MORE
THAN ATHLETIC ABILITY
 The
Patriots football team is a very
successful one
 The team uses a decision support system to
analyze the opposition’s game
 The software breaks down the game day
video into plays and player actions
 With this information the Patriots can better
formulate their strategy
4-4
AN NFL TEAM NEEDS MORE
THAN ATHLETIC ABILITY
1.
2.
3.
DSS with predictive analytics used to gain
the advantage in other sports? Choose a
sport and explain how that might work.
Would allowing coaches to have laptops on
the field change the game appreciably?
What other aspect of football could be
improved by decision support systems?
4-5
INTRODUCTION

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 of each solution
Implementation – carry out the solution
4-6
Four Phases of Decision Making
4-7
Types of Decisions You Face
decision – processing a certain
information in a specified way so that you will
always get the right answer
 Nonstructured decision – one for which
there may be several “right” answers, without
a sure way to get the right answer
 Recurring decision – happens repeatedly
 Nonrecurring (ad hoc) decision – one you
make infrequently
 Structured
4-8
Types of Decisions You Face
EASIEST
MOST
DIFFICULT
4-9
CHAPTER ORGANIZATION
1.
Decision Support Systems

2.
Geographic Information Systems

3.
Learning outcome #1
Expert Systems

4.
Learning outcome #1
Learning outcome #2
Neural Networks and Fuzzy Logic

Learning outcome #3
4-10
CHAPTER ORGANIZATION
5.
Genetic Algorithms

6.
Learning outcome #4
Intelligent Agents

Learning outcome #5
4-11
DECISION SUPPORT SYSTEMS
support system (DSS) – a highly
flexible and interactive system that is
designed to support decision making when
the problem is not structured
 Decision support systems help you analyze,
but you must know how to solve the problem,
and how to use the results of the analysis
 Decision
4-12
Alliance between You and a DSS
4-13
Components of a DSS
management component – consists
of both the DSS models and the model
management system
 Data management component – stores and
maintains the information that you want your
DSS to use
 User interface management component –
allows you to communicate with the DSS
 Model
4-14
Components of a DSS
4-15
Predictive Analytics
(predictive analytics) – highly
computational process of measuring and
predicting customer behavior/attitudes
 Uses combination of statistics, probability,
ops management methods, AI tools, data
mining, and predictive modeling
 Types
 Analytics



Text – natural language analysis
Content – audio, video, graphical
Web – Web traffic analysis
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GEOGRAPHIC INFORMATION
SYSTEMS
information system (GIS) –
DSS designed specifically to analyze spatial
information
 Spatial information is any information in map
form
 Businesses use GIS software to analyze
information, generate business intelligence,
and make decisions
 Geographic
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Zillow GIS Software for Denver
4-18
ARTIFICIAL INTELLIGENCE
 DSSs
and GISs support decision making; you
are still completely in charge
 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
(knowledge-based) system – an
artificial intelligence system that applies
reasoning capabilities to reach a conclusion
 Used for
 Expert
 Diagnostic
problems (what’s wrong?)
 Prescriptive problems (what to do?)
4-20
Traffic Light Expert System
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What Expert Systems Can and
Can’t Do
 An
expert system can
 Reduce
errors
 Improve customer service
 Reduce cost
 An
expert system can’t
 Use
common sense
 Automate all processes
4-22
NEURAL NETWORKS AND FUZZY
LOGIC
 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…
 Learn
and adjust to new circumstances on
their own
 Take part in massive parallel processing
 Function without complete information
 Cope with huge volumes of information
 Analyze nonlinear relationships
4-24
Fuzzy Logic
logic – a mathematical method of
handling imprecise or subjective information
 Used to make ambiguous information such as
“short” usable in computer systems
 Applications
 Fuzzy
 Google’s
search engine
 Washing machines
 Antilock breaks
4-25
GENETIC ALGORITHMS
algorithm – an artificial intelligence
system that mimics the evolutionary, survivalof-the-fittest process to generate increasingly
better solutions to a problem
 Genetic
4-26
Evolutionary Principles of
Genetic Algorithms
1.
2.
3.
Selection – or survival of the fittest or giving
preference to better outcomes
Crossover – combining portions of good
outcomes to create even better outcomes
Mutation – randomly trying combinations
and evaluating the success of each
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Genetic Algorithms Can…
 Take
thousands or even millions of possible
solutions and combine and recombine them
until it finds the optimal solution
 Work in environments where no model of how
to find the right solution exists
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INTELLIGENT AGENTS
agent – software that assists you,
or acts on your behalf, in performing
repetitive computer-related tasks
 Types
 Intelligent
 Information
agents
 Monitoring-and-surveillance or predictive agents
 Data-mining agents
 User or personal agents
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Information Agents
Agents – intelligent agents that
search for information of some kind and bring
it back
 Ex: Buyer agent or shopping bot – an
intelligent agent on a Web site that helps you,
the customer, find products and services you
want
 Information
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Monitoring-and-Surveillance
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
4-32
User Agents
or personal agent – intelligent agent
that takes action on your behalf
 Examples:
 User
 Prioritize
e-mail
 Act as gaming partner
 Assemble customized news reports
 Fill out forms for you
 “Discuss” topics with you
4-33
MULTI-AGENT SYSTEMS AND
AGENT-BASED MODELING


Biomimicry – learning from ecosystems
and adapting their characteristics to human
and organizational situations
Used to
1.
2.
3.
Learn how people-based systems behave
Predict how they will behave under certain
circumstances
Improve human systems to make them more
efficient and effective
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Agent-Based Modeling
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
 Multi-agent system – groups of intelligent
agents have the ability to work independently
and to interact with each other
 Agent-based
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Business Applications
Airlines – cargo routing
 P&G – supply network optimization
 Air Liquide America – reduce production and
distribution costs
 Merck – distributing anti-AIDS drugs in Africa
 Ford – balance production costs & consumer
demands
 Edison Chouest – deploy service and supply
vessels
 Southwest
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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 learning to
coherent global patterns
4-37
Characteristics of Swarm
Intelligence
– adaptable to change
 Robustness – tasks are completed even if
some individuals are removed
 Decentralization – each individual has a
simple job to do
 Flexibility
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