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.
• 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 better solution to
the problem.