Transcript Chapter 1 THE INFORMATION AGE IN WHICH YOU LIVE Changing

Chapter 4
DECISION SUPPORT AND ARTIFICIAL INTELLIGENCE
Brainpower for Your Business
McGraw-Hill
© 2008 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.
4-3
VISUALIZING INFORMATION IN MAP
FORM FOR DECISION MAKING
 Geographic
information systems (GISs) allows you
to see information spatially, or in map form.
 Researchers and scientists used a GIS to map the
location of all the debris from the shuttle Columbia
 The city of Chattanooga uses a GIS to map the
location of its 6,000 trees to help develop a
maintenance schedule
4-4
VISUALIZING INFORMATION IN MAP
FORM FOR DECISION MAKING
 The
city of Richmond, VA, used a GIS to optimize its
2,500 bus stop locations in its public transportation
system
 Sometimes, a picture is worth a thousand words
 Recall from Chapter 1, the form of information often
defines its quality
4-5
VISUALIZING INFORMATION IN MAP
FORM FOR DECISION MAKING
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?
4-6
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
4-7
Four Phases of Decision Making
4-8
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
4-9
Types of Decisions You Face
4-10
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
4-11
CHAPTER ORGANIZATION
5.
Genetic Algorithms

6.
Learning outcome #4
Intelligent Agents

Learning outcome #5
4-12
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
4-13
Alliance between You and a DSS
4-14
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
4-15
Components of a DSS
4-16
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
4-17
Zillow GIS Software for Denver
4-18
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?)

4-19
Traffic Light Expert System
4-20
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

4-21
NEURAL NETWORKS AND FUZZY
LOGIC
 Neural
network (artificial neural network or ANN)
– an artificial intelligence system that is capable of
finding and differentiating patterns
4-22
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
4-23
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

4-24
GENETIC ALGORITHMS
algorithm – an artificial intelligence system
that mimics the evolutionary, survival-of-the-fittest
process to generate increasingly better solutions to
a problem
 Genetic
4-25
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
4-26
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
4-27
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

4-28
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
4-29
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
4-30
Data-Mining Agents

Data-mining agent – operates in a data warehouse
discovering information
4-31
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

4-32
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
4-33
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
4-34
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
4-35
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
4-36
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
4-37