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Expert Systems
An expert system is a computer
program that is designed to hold
the accumulated knowledge of
one or more domain experts tosolve problems
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Background History
Expert systems first emerged from the
research laboratories of U.S.universities
during the 1960s and 1970s.
They were developed as specialized
problem solvers which emphasized the
use of knowledge rather than data andgeneral search methods.
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Background history
The first expert system to be completed
was DENDRAL, developed at stanford
university in the late 1960s.
DENDRAL: Used to
identify the structure
of chemical compounds
with consituent elements.
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Background history
DENDRAL discovered a number of
structures perviosly unknown to expert
chemists.
As researchers gained more experience
with DENDRAL,led to the development
of Meta-DENDRAL,a learningcomponent which was able to learn
rules from positive examples, a form of
inductive learning.
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Background history
PROSPECTOR:
Used by geologists
to identify sites for
drilling or mining
PUFF:
Medical system
for diagnosis of
respiratory dieses
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Background history
DESIGN ADVISOR:Gives advice to
designers of
processor chips
MYCIN:
Medical system for
diagnosing blood disorders.
First used in 1979
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Background history
The initial MYCINs knowledge base
contained only 200 Rules and
vocabulary of 2000 words.
This number was gradually increased to
more than 600 rules by the early 1980s.
This improved MYCINs performanceleading to a 65% success record which
compared favorably with experinced
physicians with 60% success rate.
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Why we use Expert Systems?
Campbell Soup uses large cookers to
cook soups and other can products at
eight plants located throghout thecountry.
Some cookers hold up to 68,000 cans
of food for short periods of cooking time.when maintenance problems occur with
the cookers,fault must be found or food
will spoil.
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Why we use Expert Systems?
Company had been depending on a
single expert to diagnose.
Expert will retire in a few years takinghis expertise with him.
so, company decided to develop an
expert system.After some months company developed
an expert system about 150 rules in KB
to diagnose cooker problems.
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Why we use Expert Systems?
Capture and preserve irreplaceablehuman expertise
Provide expertise needed at a numberof locations at the same time
Provide expertise needed for training of
human experts
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Rule based sytem architecture
In this system knowledge encoded in
the form of production rules,i.e.if .then
rules.e.g.
Inference in production systems is
done by process of chaining either
1. forwad2.backward
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Nonproduction system
architecture
Instead of rules,thes systems employ
more structured representation
schemes like
1.Neural networks 4.Desicion tree
2.Blackboard system
3.Analogical reasoning
4.frame
5.semantic network
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Blackboard system
architecture
One of the first blackboard system was
HEARSAY used for speech
understanding system.
More recently ,systems have been
developed to analyze complex scene
and model human cognitive processes.
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Blackboard system
architecture
CHINESE ROOM PROBLEM(CONGNITION)
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Neural network architecture
Neural network: information
processing inspired by biological
nervous systems, such as our brain.
Structure: large number of highly
interconnected processing elements
working together
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Control and Coordination
Reflex action
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Neuron
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Synapse
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Neuron vs node
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Training the Network -
LearningTraining the Network - Learning
Backpropagation Requires training set (input / output pairs)
Starts with small random weights
Error is used to adjust weights (supervised learning)
Gradient descent on error
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Example: Voice Recognition
Task: Learn to discriminate between
two different voices saying Hello
Data
Sources
Steve
David Format
Frequency distribution (60 bins)
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Network architecture
Feed forward network
60 input (one for each frequency bin)
6 hidden
2 output (0-1 for Steve, 1-0 for David)
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Presenting the data
Steve
David
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Presenting the dataSteve
David
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Presenting the data (untrained network)Steve
David
0.43
0.26
0.73
0.55
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Calculate errorSteve
David
0.43 0 = 0.43
0.26 1 = 0.74
0.73 1 = 0.27
0.55 0 = 0.55
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Backprop error and adjust weightsSteve
David
0.43 0 = 0.43
0.26 1 = 0.74
0.73 1 = 0.27
0.55 0 = 0.55
1.17
0.82
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Repeat process (sweep) for all training
pairs
Present data
Calculate error
Backpropagate error
Adjust weights
Repeat process multiple times
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Presenting the data (trained network)Steve
David
0.01
0.99
0.99
0.01
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Results Voice Recognition
Performance of trained network
Discrimination accuracy between known Hellos
100%
Discrimination accuracy between new Hellos
100%
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Problems with Expert Systems
Limited domain
Systems are not always
up to date, and dontlearn
No common sense
Experts needed to setupand maintain system
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Legal and Ethical Issues
Who is responsible if the advice is wrong?
The user?
The domain expert?
The knowledge engineer?
The programmer of the expert system shell?
The company selling the software?