Transcript Decision support system - Austin Community College

Chapter 4
Decision Support and Artificial
Intelligence
McGraw-Hill/Irwin
© 2008 The McGraw-Hill Companies,
All Rights Reserved
STUDENT LEARNING OUTCOMES
1. Define decision support system, list its
components, and identify the type of
application it’s suited to.
2. Define geographic information systems and
state how they differ from other decision
support systems.
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STUDENT LEARNING OUTCOMES
3. Define artificial intelligence and list the
different types that are used in businesses.
4. Define expert systems and describe the types
of problems to which they are applicable.
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STUDENT LEARNING OUTCOMES
5. Define neural networks and fuzzy logic and
the uses of these AI tools.
6. Define genetic algorithms and list the
concepts on which they are based and the
types of problems they solve.
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STUDENT LEARNING OUTCOMES
7. Define intelligent agents and list the different
types that are used in businesses.
8. Define agent-based modeling and swarm
intelligence.
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Decision Support System – The
Resident Opinion
• Cleveland Clinic uses automated DSSs when
diagnosing patient illnesses
• All hospital databases are tied together,
making it possible for doctors to compare each
new illness and patient with all relevant
previous cases
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Decision Support System – The
Resident Opinion
• By being able to compare data on illnesses as
well as other data such as demographics, the
clinic can better pinpoint the best treatment
• The computer-aided decision support that the
Cleveland Clinic uses includes data-mining
and neural network techniques
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Decision Support System – The
Resident Opinion
• Class poll…
– What IT concepts can you identify for the
Cleveland Clinic?
– Do you foresee a health care system where you’d
do most of the diagnosis and arrange the
treatments yourself with/through IT?
– Is there a downside to predicting which patients
are more likely to fall victim to specific diseases?
(Hint: You might think about what you learned in
you statistics courses.)
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INTRODUCTION
• Computer-aided decision support
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DECISIONS, DECISIONS,
DECISIONS
• 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
• Structured 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
• Sometimes you satisfice – make a decision that
is satisfactory but not necessarily the best
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What Job Do I Take?
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Types of Decisions You Face
• Recurring decision – one that happens
repeatedly
• Nonrecurring (ad hoc) decision – one you
make infrequently
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DECISION SUPPORT SYSTEMS
• Decision 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
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Alliance between You and a DSS
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Components of a DSS
• Model 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
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Components of a DSS
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GEOGRAPHIC INFORMATION
SYSTEMS
• Geographic 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
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San Diego in GIS Software
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ARTIFICIAL INTELLIGENCE
• Artificial intelligence (AI) – the science of
making machines imitate human thinking and
behavior
• Robot – a mechanical device equipped with
simulated human senses and the ability to take
action on its own
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ARTIFICIAL INTELLIGENCE
• Types of AI systems used in business
1.
2.
3.
4.
Expert systems
Neural networks
Genetic algorithms
Intelligent agents
• AI systems deliver the conclusion (rather than
helping you analyze the options)
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EXPERT SYSTEMS
• Expert (knowledge-based) system – an
artificial intelligence system that applies
reasoning capabilities to reach a conclusion
• 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
• An expert system can
– Reduce errors
– Improve customer service
– Reduce cost
• An expert system can’t
– Use common sense
– Automate all processes
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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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Layers of a Neural Network
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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
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Fuzzy Logic
• Fuzzy logic – a mathematical method of
handling imprecise or subjective information
• Used to make ambiguous information such as
“short” usable in computer systems
• Applications
– Google’s search engine
– Washing machines
– Antilock breaks
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GENETIC ALGORITHMS
• Genetic algorithm – an artificial intelligence
system that mimics the evolutionary, survivalof-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…
• 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
• Intelligent agent – software that assists you, or
acts on your behalf, in performing repetitive
computer-related tasks
• Types
–
–
–
–
Information agents
Monitoring-and-surveillance or predictive agents
Data-mining agents
User or personal agents
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Information Agents
• Information 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
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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
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User Agents
• User or personal agent – intelligent agent that
takes action on your behalf
• 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
• Biomimicry – learning from ecosystems and
adapting their characteristics to human and
organizational situations
• 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
• 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
• 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
• Southwest 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
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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
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Characteristics of Swarm Intelligence
• Flexibility – adaptable to change
• Robustness – tasks are completed even if some
individuals are removed
• Decentralization – each individual has a simple
job to do
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Ants A and B Leave the Same Point to
Search for Food and Leave Trails
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Ant A Finds a Food Source First and
Returns to the Nest Leaving a Trail
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Other Ants Follow Ant A’s Trail and
Ant B’s Trail Evaporates
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CAN YOU…
1. Define decision support system, list its
components, and identify the type of
application it’s suited to.
2. Define geographic information systems and
state how they differ from other decision
support systems.
3. Define artificial intelligence and list the
different types that are used in businesses.
4-46
CAN YOU…
4. Define expert systems and describe the types
of problems to which they are applicable.
5. Define neural networks and fuzzy logic and
the uses of these AI tools.
6. Define genetic algorithms and list the
concepts on which they are based and the
types of problems they solve.
4-47
CAN YOU…
7. Define intelligent agents and list the different
types that are used in businesses.
8. Define agent-based modeling and swarm
intelligence.
4-48