Download agents - psu-is101

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.
Intelligence (find what to fix)


2.
find or recognize a problem, need, or opportunity (the
diagnostic phase).
Detect and interpret signs that indicate a situation
which needs your attention.
Design (find fixes)


consider possible ways of solving the problem.
Develop all the possible solutions
4-7
INTRODUCTION
Phases of decision making (Cont.)
3.
Choice (pick a fix)


4.
weigh the merits of each solution and choose the best
one.
At this stage a course of action is prescribed.
Implementation (apply the fix)


carry out the chosen solution, monitor the results and
make adjustments as necessary.
Your solution will always need fine-tuning.
4-8
Four Phases of Decision Making
4-9
Types of Decisions You Face
 Structured
decision
Processing a certain information in a specified way so
that you will always get the right answer
 E.g. calculating gross pay for hourly workers.
 Can be easily automated with IT.

 Nonstructured
decision
One for which there may be several “right” answers,
without a sure way to get the right answer
 E.g. introduce a new product line, employ a marketting
campaign.
 What about choosing a job?

4-10
Types of Decisions You Face
 Recurring
decision
Happens repeatedly (weekly, monthly, quarterly, or
yearly)
 E.g. deciding how much inventory to carry, at what
price to sell the inventory.

 Nonrecurring
(ad hoc) decision
One you make infrequently (might be once)
 E.g. deciding where to build a distribution center,
company mergers.

4-11
Types of Decisions You Face
4-12
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
4-13
CHAPTER ORGANIZATION
5.
Genetic Algorithms

6.
Learning outcome #4
Intelligent Agents

Learning outcome #5
4-14
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.
4-15
Alliance between You and a DSS
The union of your know-how and IT power helps you
generate business intelligence so that you can quickly
respond to changes and manage resources in the most
effective and efficient ways
possible.
4-16
Components of a DSS
A typical DSS has three components:
1.
2.
3.
Model management component
Data management component
User interface management component
 When
you begin your analysis, you tell the DSS,
using the user interface management component,
which model (in the model management component)
to use on what information (in the data management
component). The model requests the information
from the data management component, analyzes it
and sends the result to the user interface
management component which passes the results
4-17
back to you.
Components of a DSS (Cont.)
1.
Model management component





Consists of both the DSS models and the model
management system
A model is a representation of some event, fact, or
situation.
Businesses use models to represent variables and
their relationships.
E.g. you would use a statistical model called analysis
of variance to determine whether newspaper and
television are equally effective in increasing sales.
The model management component can’t select the
best model for you to use for some problem but it can
help you create and manipulate models quickly and
easily.
4-18
Components of a DSS (Cont.)
2- Data management component
Stores and maintains the information that you want
your DSS to use
 Consists of both the DSS information and the DSS
DBMS.
 This information can come from one or more of three
resources:

1.
2.
3.
Organizational information
External information, e.g. federal government, Dow
Jones and the Internet.
Personal information- your own insights and
experience.
4-19
Components of a DSS (Cont.)
3- User interface management component
Allows you to communicate with the DSS
 Consists of the user interface and the user interface
management system.
 Allows you to combine your know-how with the
storage and processing capabilities of the computer.
 This the part that you see, through it you enter
information, commands and models.

4-20
Components of a DSS
4-21
GEOGRAPHIC INFORMATION
SYSTEMS
 Geographic
information system (GIS) – DSS
designed specifically to analyze spatial information.
 Spatial
information is any information in map form
such as roads, the path of a hurricane, etc.
 Businesses
use GIS software to analyze
information, generate business intelligence, and
make decisions.
4-22
Zillow GIS Software for Denver
4-23
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?)  correspond to
the intelligence phase of decision making.

Prescriptive problems (what to do?)  correspond to
the choice phase of decision making.
4-24
EXPERT SYSTEMS (Cont.)
 What’s
the difference between a DSS and an
expert system?
 To
use a DSS, you must have considerable
knowledge or expertise with the situation
A
DSS assists you in making decisions.
 You
 In
must know how to reason things.
an expert system the know how is in the system.
 You
need only to provide the facts and symptoms of
the problem.
4-25
Traffic Light Expert System
4-26
What Expert Systems Can and Can’t
Do
 An
expert system can
Handle massive amounts of information
 Reduce errors
 Aggregate information from various sources
 Improve customer service
 Decrease personnel time spent on tasks
 Reduce cost

 An
expert system can’t
Use common sense
 Automate all processes

4-27
NEURAL NETWORKS AND FUZZY
LOGIC
 Neural
network (artificial neural network or ANN)
– an artificial intelligence system that is capable of
finding and differentiating patterns
 A neural network can learn by example and can
adapt to new concepts and knowledge.
 Neural networks are widely used for visual pattern
and speech recognition systems.
 Neural networks are called predictive systems since
they can see patters in huge volumes of information.
 See examples on page 109.
4-28
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
4-29
Fuzzy Logic
A
way of reaching conclusions based on ambiguous
or vague information. E.g. temperature.
 Fuzzy
logic – a mathematical method of handling
imprecise or subjective information
 Used
to make ambiguous information such as
“short” usable in computer systems
 Examples:
Google’s search engine, washing
machines, etc.
4-30
GENETIC ALGORITHMS
algorithm – an artificial intelligence system
that mimics the evolutionary, survival-of-the-fittest
process to generate increasingly better solutions to
a problem
 A genetic algorithm is an optimizing system  it
finds the combination of inputs that give the best
output.
 Genetic
4-31
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
4-32
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
4-33
INTELLIGENT AGENTS
agent – software that assists you, or
acts on your behalf, in performing repetitive
computer-related tasks
 E.g. the animated paper clip in MS Word that offers
suggestions on how to proceed in writing a letter.
 Types
 Intelligent
Information agents
 Monitoring-and-surveillance or predictive agents
 Data-mining agents
 User or personal agents

4-34
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 (Amazon.com)
 Ex:
A CNN Custom News Bot will gather news from
CNN on the topics you want to read about.
4-35
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.
E.g:
Agents that monitor complex computer networks to
predict for system crashes before they happen.
Agents that monitor Internet sites, discussion
groups, mailing lists, etc., for stock manipulation.
Agents that monitor sites for updated information on
the topic of your choice.
Agents that monitor auction sites for products or
sites that you want. 4-36
Data-Mining Agents

Data-mining agent – operates in a data warehouse
discovering information

A data-mining agent may detect major shifts in a
trend or a key indicator.

E.g. Volkswagen’s intelligent agent system might
see a problem in some part of the country that is
about to cause payments to slow down. Having this
information, managers can formulate a plan to
protect themselves.
4-37
User Agents
or personal agent – intelligent agent that
takes action on your behalf
 Examples:
 User
Prioritize e-mail
 Act as gaming partner
 Fill out forms for you
 “Discuss” topics with you

4-38
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
4-39
Agent-Based Modeling
system – groups of intelligent agents
have the ability to work independently and to
interact with each other.
 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.
 E.g. Agent-based modeling systems are being used
to predict the escape routes that people seek in a
burning building.
 Multi-agent
4-40
Companies that Use
Agent-Based Modeling
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
4-41
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
4-42
Characteristics of Swarm Intelligence
1.
2.
3.
4.
Flexibility – adaptable to change (small or big) in
the environment around it.
Robustness – tasks are completed even if some
individuals are removed  if some members don’t
succeed, work gets done.
Decentralization – each individual has a simple job
to do and performs it without supervision.
Self-organization – methods of problem solving
are not prescribed from a central authority, but
rather developed by the individuals who are
responsible for completing the task.
4-43