Artificial
Intelligence
and
Expert
Systems
ARTIFICIAL INTELLIGENCE (AI)
is the science of
• R
• L
• Being able
to
• Ability to
solve a
problem
Comparing a DSS to Artificial
Intelligence
• Decision Support System (DSS)
– User actively involved with the system.
– Relies on
. The user must understand
problem situation and what needs to be done.
– The user makes the ultimate decision/choice.
• Artificial Intelligence
– User not as actively involved because all of the expertise
is
– The system makes the ultimate decision/choice.
Robotics
• E
Systems are
that imitate the reasoning process of experts. They
consist of a knowledge base and a set of rules for
applying that knowledge base to a particular situation.
Most common form of AI in business.
• Neural Networks mimic the way the brain works,
analyzing large quantities of data and information to
establish
.
• Genetic algorithms mimic the evolutionary, survivalof-the-fittest process to
increasingly
. Genetic algorithms
work to find the
answer.
• Intelligence agents accomplish a specific task for the user.
AN EXPERT SYSTEM
is an artificial intelligence system that
applies
to reach a
conclusion.
An expert system captures expertise from a
human expert and applies it to a problem.
Tricks of the trade
Knowledge base
Reasoning Process
Expert Systems
• Programming is in the form of
and Reasons
• Decision Support System guides you, but you
must reason through the problem.
• Expert Systems : you provide the facts, it
• Used as diagnostic and prescriptive.
Expert System Rules for a Bank Mortgage Application
Example of Medical
Expert System for lung
cancer treatment
If lung capacity is high
AND X-ray results are positive
AND patient has fever
AND patient has coughing
THEN surgery is necessary.
If tumor has spread
OR contraindications to surgery exist
THEN surgery cannot be performed
Traffic Light Expert System
Expert Systems
• Expert Systems are computerized advisory programs
that imitate the reasoning process of experts. They
consist of a knowledge base and a set of rules for
applying that knowledge base to a particular situation.
• EXPERT SYSTEMS
.
– The system uses IF statements and user answers to
questions in order to reason just like a human does.
– It takes something the users doesn’t know and applies rules
to indicate what to do.
• Expert Systems:
to determine what is “known.”
Easy
Diagnosis
Medical
Expert
System
WHAT EXPERT SYSTEMS CAN DO
• Can handle massive
amounts of information
and they can
• Can
from complex
relationships
• Can explain their
reasoning or suggested
decisions
• Provide
decision making.
in
• Improve customer
service.
• Reduce errors and costs.
• Provide
WHAT EXPERT
SYSTEMS CAN’T DO
• Handle all types of domain expertise. Human
experts might not fully be aware of the process
that they use. Can’t put everything into
machine form.
• Can’t solve problems in areas not designed for.
Can’t
• Apply
or judgment to a problem
Expert Systems Perform
and
Tasks Like
•
•
•
•
•
•
Expert System used
Auditing and tax planning
by American
Diagnosing illnesses
Express’ Optima
Card program.
Managing forest resources
VB Loan
Evaluate credit and loan applications
System
Computer help desk diagnosis assistance
Rules to follow when directing air traffic
Smartflow
Acquired Intelligence
Whale Watcher
Douglas Fir Cone and Seed
Exsys Corvid
Which Dog Breed is best for you?
Marathon Race Advisor
Albuquerque Restaurant Advisor
Web Support
Camcorder Selection
Ethical Questions and the Use of
Expert Systems
• An expert system will act as it is programmed. If
you program in bias, then the system will be
biased.
• The expert system is consistent, which is easily
defended in court.
• Can distinguish between good and bad, but may
not be able to distinguish between degrees of
good.
• Expert Systems are computerized advisory
programs that imitate the reasoning process of
experts.
– EXPERT SYSTEMS apply rules to solve a problem.
– Expert Systems: ask a series of questions to determine
what is “known.”
• Neural Networks mimic the way the brain works,
analyzing large quantities of data and information to
establish patterns and infer relationships.
– They
• They can “see” subtle, hidden and newly emerging patterns
within large amounts of complex data.
A NEURAL NETWORK
is an artificial intelligence system which is
capable of learning because it’s patterned
after the human brain. Uses parallel
processors.
A neural network simulates the human ability to
classify things based on the experience of seeing
many examples.
Learn by
NEURAL NETWORKS
• Typically used to combat attempts at fraud
•
Credit card fraud or insurance fraud.
• Able to detect money laundering attempts.
• Working in conjunction with X-ray machines, can be
used to detect weapons and other forbidden items.
• Often used to make investment decisions (stocks,
bonds, futures markets, etc.)
•
Can also detect inefficiencies in financial markets
Learn by looking at a data set and finding patterns in it.
A Neural Network Can Perform
Tasks Like
• Distinguishing different chemical compounds
• D
in human tissue
that may signify disease
• A
to detect forgeries.
• De
• Track habits of insurance customers and predict
which ones might not renew their policies
• Virus Detection Software by IBM
• Neugent monitors 1,200 data points in the Allstate
Insurance network every 5 seconds, trying to
predict a potential problem in/with the network.
Neural networks attempt to mimic the structure and
functioning of the human brain. They contain input, output
and hidden layers. The hidden layers use various weights of
strength to
. As the system
,
it can change the classification weights.
Neural networks can adjust or change themselves
over time based upon data input regarding
successful and unsuccessful mortgage applications.
Neural networks
as they
“learn”. Expert systems
.
Neural Networks serve as
Systems
• Allows the computer to
or
it receives.
• There are computer games with learning abilities.
• 20Questions www.20Q.net
• F
and neural networks are often
combined to express complicated and
concepts (that are
and ambiguous) in a
form that makes it possible to simplify the
problem and apply rules with some degree of
certainty.
Fuzzy Logic
• Fuzzy Logic: a special field of computer science that
and does not require conditions to be
• A mathematical method of handling
information so that ambiguous information such as “
” or “
” or other “non-exact areas
usable in computer systems
• Applications
–
–
–
–
–
–
Google’s search engine (your perception of a topic frames your query)
Washing machines that wash until the water is “clean”
A
and subway/tram control systems
A
cameras
Temperature sensors attached to furnace controls
Medical equipment that
based upon
patient vital signs.
– Accounting: how do you value intangible assets such as
• EXPERT SYSTEMS apply rules to solve a problem.
– The system uses IF statements and user answers to questions in order
to reason just like a human does.
– It takes something the users doesn’t know and applies rules to indicate
what to do.
– Expert Systems: ask a series of questions to determine what is
“known.”
• NEURAL NETWORKS recognize/learn patterns and can apply
that learning to the unknown.
– It is either taught by someone or teaches itself. After it is taught to
recognize the pattern, it can adjust itself to reflect new learning.
– Neural networks: system is “guessing” based upon examples and
patterns found in the data set- trying to figure out what category
something fits in.
• GENETIC ALGORITHMS generate several generations of
solutions, with each generation resulting in a
to the problem.
A GENETIC ALGORITHM
is an artificial intelligence system that mimics
the
to generate
increasingly better solutions to a problem.
Genetic algorithms produce several generations
of solutions, choosing the best of the current set
for each new generation.
THE CONCEPTS OF EVOLUTION
IN GENETIC ALGORITHMS
•
- or survival of the fittest. The
key is to give preference to better outcomes.
•
- combining portions of good
outcomes in the hope of creating an even
better outcome.
•
- randomly trying combinations
and evaluating the success (or failure) of the
outcome.
Seeking an
Genetic Algorithms Can Generate Lots of
Solutions As In
• Deciding which
given limited investment dollars.
a firm should invest in,
• Generating solutions to
– How much cable or track to lay?
– What
should your delivery vehicles take?
• Used to
(make the best use of your
production resources)
• Investment companies use them to generate
by considering
and bonds .
• Clothing manufacturing:
generate the
www.coyotegulch.com:
of stocks
so as to
The Traveling Salesman
AN INTELLIGENT AGENT
is a
that
and
then
with a certain degree of
, and in doing so,
employs knowledge or representation of the user’s
goals or desires.
The Agent will take your profile and preferences and
then go out and work on your behalf.
Characteristics of an intelligent agent
A
A
: can act without you telling them what to do
: can
and what it does based
upon your changing characteristics.
S
: can
and
agents that it encounters.
with other
Types of Intelligent Agents
• I
Internet or a database)
– B
s, shopping bots,
and bring it back to you (from the
, Googlebots that scour the
Internet locating and indexing sites that ultimately appear in search results when you do a
Google search.
– Information agents for Amazon display lists of books and other products that
customers might like, based on past purchases.
• M
and Surveillance Agents: constantly
– A
and offer suggestions for improvement.
– Agents that monitor web sites for updated info, such as price changes on desired
products.
– Wizards in Microsoft Office
• U
: act as a personal assistant by
. Examples include sorting and prioritizing email, filling out forms on
the Web automatically for you, and automatically storing your information.
• D
agents operate in a data warehouse by sifting through the
data, trying to discover trends, relationships and patterns through the use of
multidimensional statistical analysis.
Monitoring & Surveillance Agents:
constantly observe and report back on what they see.
• Spell Checker
• Grammar Checker
• Monitoring and
surveillance agent
in Excel
Data-mining agents perform
multidimensional analysis in data
warehouses
• Cube – common term for the representation of multidimensional information (layers, rows, columns)
• EXPERT SYSTEMS apply rules to solve a problem.
– The system uses IF statements and user answers to
questions in order to reason just like a human does.
– It takes something the users doesn’t know and applies rules
to indicate what to do.
• NEURAL NETWORKS recognize/learn patterns and
can apply that learning to the unknown.
– It is either taught by someone or teaches itself. After it is
taught to recognize the pattern, it can adjust itself to reflect
new learning.
• GENETIC ALGORITHMS generate several
generations of solutions, with each generation
resulting in a better solution to the problem.
• Expert Systems: ask a series of questions to
determine what is “known.”
• Neural networks: system is “guessing” based
upon examples and patterns found in the data
set- trying to figure out what category
something fits in.
Based On
Starting
Information
AI System
Problem Type
Expert
Systems
Diagnostic or
prescriptive
Strategies of
experts
Expert’s
know-how
Neural
Networks
Identification,
classification,
prediction
The human
brain
Acceptable
patterns
Genetic
Algorithms
Biological
Optimal solution evolution
Set of
possible
solutions
Intelligent
Agents
Specific and
repetitive tasks
Your
preferences
One or more AI
techniques