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• 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