expert system and neural networks

8/7/2019 expert system and neural networks

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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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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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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?

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expert system and neural networks