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INTELLIGENCE DECISION SUPPORT SYSTEM
TITLE 9: INTELLIGENCE DECISION SUPPORT
SYSTEM
Rossilawati Sulaiman
INTRODUCTION
In this chapter we are going to discuss Artificial Intelligence techniques that can be used in
decision-making. Our concern is to enhance our decision support system, especially in the
decision making process. We will explore briefly the fields in AI, and discuss more on
artificial neural network and expert system. Some examples are also given on the
applications that use AI technique in supporting decision-making process.
OBJECTIVES
After you have studied this chapter, you will learn:
1. the fields in Artificial Intelligence
2. why we use AI technique in decision making
3. the use of expert system in making decision
4. the use of artificial neural network technique in decision making
5. examples of applications using AI techniques in decision-making.
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MIND MAP
AI DSS
Definition
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9.1 ARTIFICIAL
DEFINITION
INTELLIGENCE:
CONCEPT
AND
The are many definitions of Artificial Intelligence (AI).
Stuart-russel:
Tazmania:
Basically, Artificial Intelligence indicates the criteria that a machine has, which can imitate
human thought process. AI can also be referred to the ability of ‘learning’, ‘reasoning’ and
making decision.
Why do we need AI elements in implementing DSS? Computers are designed to perform a
task from the easiest to the most highly complicated one, based on people needs.
However, computers are not able to learn from experience like human do. Whenever a
decision is going to be made, computers cannot simply ‘think’ or assess any
consequences out of it. Artificial Intelligence aims to enhance machine behavior so that an
intelligent element can be embedded into the computer during the decision making
process and problem solving. Therefore, the computer will imitate human by taking into
account every factor presented to it. Furthermore with AI added in, computer can then
learn from experience like humans do and this criterion will assist in making a better
decision.
9.1.1 Some Artificial Intelligence Fields
This section will briefly describe on some Artificial Intelligence areas. These concepts
usually applied in different domain of problem.
EXPERT SYSTEM
This system uses human expertise stored in the system. The system will interact with
users to get information and solve the problem. We will go into detail on the system later in
this chapter.
NATURAL LANGUAGE PROCESSING
The goal of the Natural Language Processing (NLP) technology is to allow computer user
to communicate with computer using languages that they use naturally and finally users
can communicate to the computer like they communicate with another person.
SPEECH RECOGNITION
This technology is the ability of the computer to recognize and understand the spoken
language. The computer must able to understand anyone’s speech and react to voice
command.
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NEURAL COMPUTING
Neural Computing technology is based on human brain functionality that is transformed
into mathematical model. This model is used in many areas such as in medical, business,
transportation and many more. We will discuss this topic later in this chapter.
GAMES
There are many AI techniques applied in this area. For example the famous Deep Blue, a
chess game application with intelligent feature embedded in it.
FUZZY LOGIC
This technique applied to deal with fuzziness of a fact. Fuzzy logic goes further than
Boolean true/false. It will extend the fact to be partially true (or partially false) with certain
degrees.
GENETIC ALGORITHM
This technique used in searching for pattern from a set of data. This technique is based on
biological evolution of genetic variation.
INTELLIGENT AGENT
This technique used a small program that will be release to a network to do certain task
automatically. A very simple example is an antivirus program that is automatically detects
any unwanted program in a computer. Other application that is using agent is to do bidding
in an online auction.
Exercise 9.1
4
1.
Why do we need AI techniques in implementing Decision Support System?
2.
Give 5 fields that are currently used in AI.
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9.2 EXPERT SYSTEM
An expert system is a technique in AI that can enhance a decision-making process.
Basically, an expert, a person with special skill or knowledge will ‘give’ his knowledge to
the system so that the system will ‘think’ like a human when making decision. In this
section, we will discuss about the general architecture of an expert system, specifically in
knowledge based system. Following that we will discuss about expert system applications
and how they assist human in making decision.
9.2.1 THE GENERAL ARCHITECTURE OF EXPERT SYSTEM:
KNOWLEDGE BASED SYSTEM
CONSULTATION ENVIRONMENT
DEVELOPMENT ENVIRONMENT
Knowledgebased
Rules/Facts
User
Explanation
Services
Knowledge
Engineer
Interfaces
Inference Engine
Suggestion
on actions
Knowledge
Expert
Documented
Information
Figure 9.1: Structure of an Expert System
Figure 9.1 shows the architecture of an expert system. Generally, an expert system
contains two major environments; the development environment and consultation
environment, an inference engine and the knowledge base.

Development Environment
In the development environment, the knowledge obtained from the expert, together with
other documented information will be transferred to set of rules by knowledge engineers.
The rules will be kept in the knowledge base.
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
Knowledge Base
Knowledge base will store rules from experts as well as facts related to the domain
problem. Table 9.1 below shows an example of rules describing ischaemic heart disease
depicted from [arieff,2004]. All facts about this disease will be collected from heart experts
and then transformed into rules.
Table 9.1: Rules developed to describe inflammatory heart disease
Rules for ischaemic heart disease
IF
You have high blood pressure
AND
You have a diabetes
AND
You have hypercholesterolemia
AND
You feel pain in the chest for a few minutes
AND
You have nausea/vomiting
THEN
Ischaemic heart disease

Inference Engine
An inference engine is used to infer the rules in the knowledge base during the decisionmaking process. The engine will infer the rules according to the fact given by users and
come out with a conclusion.

Consultation Environment
In the consultation environment experts in the domain-specific problem will assist the nonexperts, or the target user. The explanation service will explain in further detail how the
system comes out with a particular solution. The interaction between end-user and the
system is done through the interface.
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9.2.2 APPLICATIONS ON EXPERT SYSTEM
There are many expert system applications applied in various kinds of field. Below are
examples on applications that are developed using expert system technique.
R1/XCON
This system was developed by John McDermott et.al. in late 70’s. The system is said to be
the most successful commercial expert systems. It is developed as to help in setting
configuration in VAX computer system at Digital Equipment Corporation. The configuration
is on the basis of customer’s need. The input of the system is the computer characteristics
required by users. The output would be a suggested decision on the computer
specification, so that users can make decisions to buy computer specification according to
their requirements. However, the system has a few shortcomings where the rules have
expanded from time to time. New rules will be simply added in the rulebase and after a
while the rulebase become very large. Consequently the system was no longer reliable
and the company needs to rewrite the system to get better result.
MYCIN
MYCIN is another application in expert system specifically in medical area. It is developed
using LISP programming language by Edward Shortliffe in late 70s. This application helps
in diagnosing infection in blood-related diseases. The input of the system is the symptoms
of the specified disease and the output is the diagnosed disease together with the degree
of certainty and the suggested therapy. The system will ask series of questions to the user
as inputs. This application uses more complex questions to get inputs from users. The
inference engine uses the rules in the rulebase during the diagnosing process based on
the inputs given by the user.
E-PADDY
E-PADDY is a diagnosis and advisory system developed by a group of researchers in
National University of Malaysia [ROSS et. al]. The system covers paddy diseases and pest
control diagnosis on paddy plant. Up to now, the system is able to diagnose 18 possible
diseases, using production rules and frame approach. The system will request input from
the user, to get symptoms of a specific disease by presenting series of questions to the
user. The input given will be matched to the set of rules in the knowledge-base to produce
the specific output regarding the disease.
Exercise 9.2
1.
What is the role of inference engine in an expert system?
2.
Give two examples of applications that use expert system technique and
describe how they help in making decision
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9.3 MACHINE LEARNING
Another technique in AI that can be used in DSS is Machine Learning. This technique
allows machine to learn or acquire knowledge from experience (such as historical data).
Basically, there are two types of categories: supervised and unsupervised learning.
Supervised learning indicates a process of learning that induces knowledge from a set of
data, which the final outcomes are known. For example, we induce a set of rules from
historical paddy diseases data. We already know all possible cases of diseases that might
occur. Meanwhile, unsupervised learning is used to obtained knowledge from a set of
data, which the final outcomes are unknown. For example, we can do classification on
users’ preferences of different products. We do not know what kind of choices the user
might select. Figure 9.*** below indicates a taxonomy of learning machine, depicted from
[efram Turban]
Machine
Learning
Supervised
Explanationbased learning
Case-based
reasoning
Unsupervised
Statistical
regression
Inductive
learning
Genetic
Algorithm
Neural
Network
Genetic
Algorithm
Neural
Network
Clustering
Figure 9.2: Taxonomy of Learning Methods
From Figure 9.2, we can identify several methods and algorithms used in machine
learning:
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
Inductive Learning
Inductive learning involves the process of learning by example. The system will do a
generalization from a specific observation. The generalization can be used for
explanatory or predictive purposes. For example in explanatory purpose, the system
will produce a set of similarities from a given set of data. For instance, a customer
buying-behavior study discovers that if customers buy sausage and bread, it is likely
that they will also buy butter. In predictive purpose, the system will learn from a set of
data that is classified into two or more classes. For example, the system is required to
classify different types of animals.

Case-based reasoning
A case-based reasoning (CBR) system works by finding solution to new problem from
a historical database and then adapting successful solutions from the past to current
situations. For example, if a computer does not start, CBR can be used to match the
characteristics of the problem with a database.

Neural computing
Neural computing is a technique that attempts to imitate the structure and functionality
of the human brain. The system uses this technique will be presented to a set of
training data so that the system can learn to solve a specific problem. More on this will
be further explained in section**.

Genetic algorithm
Inspired by Darwin's theory of evolution, this technique solves problem by using
evolutionary process. Initially the system is presented by a set of solutions that is
represented by chromosomes. The set is also called population. The system will try to
find the right solution from the population. The chosen solutions will be used to form a
new population, which is possibly better than the previous one. The selected solutions
are based on their fitness. This process is evolving until some condition is satisfied.



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Clustering –put data into several groups based on their similarities, used for
marketing etc
Statistical methods-such as multiple discriminant analysis, suitable for analyzing
knowledge that is quantitative in nature and have been applied to knowledge
acquisition, forecasting, and problem solving
Explanation-based learning-combine exixting theories to explain why one instance
is not a prototypical member of a class
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We have discussed so far that there are many methods and algorithms used under
machine learning that can be an aid to decision support. In the next section, we will be
discussing about neural network method in detail and how it can be used to support
decision-making.
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9.4 ARTIFICIAL NEURAL NETWORKS (ANN)
The real human brain biological process initially inspires Artificial Neural Networks (ANN).
ANN is one of the AI techniques that can support decision-making. ANN can learn from
experience like human do. Generally, neural networks are used in pattern recognition,
generalization, and prediction. Neural networks are trained by showing them examples
data repeatedly. The data contain the input and the desired output for a particular problem.
The network will be trained to learn to achieve the desired output. When the network has
succeeded in mastering the learning process, we can test the network by a new test data
that the network never seen before. At this stage, the network should still be able to predict
the correct output.
Neural network software packages are widely used in business especially in stock market
prediction, credit card fault detector and many more. In medical area, neural networks are
widely used in health monitoring and diagnosing diseases. You can browse
http://www.calsci.com/Applications.html for more examples of neural network
applications.
9.4.1 Basic component of ANN
ANN consists of several interconnected simple processors as a parallel computing
procedure. Each processor (also can be referred to as a node or neuron) is only concern
on signals it sends to and signals it receives from other processors periodically. Each
processor will be cooperating with other processors in a large network to perform the
required task. For example, in business area neural network can be used to help us decide
whether a candidate is qualified for a bank loan. There are also applications that can help
us forecast the stock market prices.
 The processing unit
Now let us take a look at the basic component of ANN. Figure 9.1 below shows a single
neuron in a network. The neuron will receive several signals as input from other connected
neurons as well as sending its signal to the other neurons in the network as an output. The
output signals sent to the other connected units are also known as weights. Each neuron
will have the capability to compute the combining input signals. An activation rule in each
unit will later compute the output signal using the combining input value. The output will be
the input to other neurons, but it might as well be the final result such as 1 as YES and 0
as NO.
X
1
X
2
Xi
Input signals (weights)
from other processors
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W1j
W2j
Wij
Net j=
∑Wij . xi
Neuron
j
Activation
function
ƒ(x
)
Yj
Output signals
(weights) to
other processors
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Figure 9.3: A single processor in a network
From figure 9.1, x1, x2,…, xi are the values of input neurons, with weights values of w1j,
w2j,…, wij respectively. The calculation of the combined input Netj, is the summation of
multiplication of input value xi with the respective weights wij.
n
Netj =
Wij.xi
i 1
where n is the number of connected neurons, Wij is the weight sent to the neuron j and xi
is the values of input neuron. There is also another function to calculate the output signal,
called the activation function, f(x). This function is actually to normalized the value of
combined input value, Netj that later be the final output value for the neuron. This output
value will either be sent to the other neurons as input value or it might be the final output
for the network.
EXAMPLE 9.1
This example will show the calculation of combined input Netj for neuron j illustrated in
figure 9.**.
0.2
0.5
2.4
0.4
j
0.3
0.2
n
Netj
=
Wij.xi
i 1
= (0.5 X 0.2) + (0.4 X 2.4) + (0.2 X 0.3)
= 1.12
or alternatively, we can use matrices like the following:
 0 .2 
0.5 0.4 0.2 2.4 = 1.12
 0.3
Figure 9.4: An example of calculating the combining output in a neuron
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Exercise 9.3
Calculate the combined input for the following neuron:
X1 = 3
X2 = 5
X3 = 1
W1=0.2
W2=0.4
Neuron j
W3=0.7
 The network
One of the main aspects to be considered in ANN is how to construct a network, which
requires us to concern on the connectivity among neurons, including the direction of the
connections as well as their respective weight values. Each ANN consists of neurons
arranged in layers. Basically, there can be three layers of neurons illustrate in figure 9.***
below. There are input layer with four neurons as input units, middle layer with two
neurons, which is also known as hidden layer, and output layer with one neuron as output
unit. It is called input unit because it will take input directly from the environment like
keyboards, sensors and so on. The same thing applied to output unit, which will send
output directly to the environment. Meanwhile, middle layer or the hidden layer consists of
hidden units and they are not directly connected to the environment.
Each neuron is connected to the other neurons in the adjacent layer. There can be more
than one hidden layer depends on how we model our network. The neurons can be
connected to one another in various ways. Each neuron can be connected to every other
neuron, or each neuron can only be connected to the adjacent neuron on the other layer.
Also, there are models that allow feedback connection to the other neuron on the adjacent
layer and so on.
Input layer
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Hidden layer
Output layer
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Figure 9.5: A Neural Network with single hidden layer
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 Activation Functions
An activation function in a network will carry out a task of calculating the output signal of a
neuron. The calculated output signal then sent to the other neurons. The final output could
be a real number, or real number within a tolerance interval [0-1], or a discrete number like
{0,1} or {-1,+1}. A simple example of activation function is the Sigmoid function, which is
commonly used in ANN. The output from this function is in the range of 0 to 1. An example
is shown in Figure 9.*** below. The function is involve is:
Yj 
1
.
1  exp(  Netj)
Yj is the normalized value of combined input value Netj, typically between 0 and 1. This
process is performed before the output sent to the other neuron in the next layer. If the
normalization is not done, the output will be very large and it is difficult to reached the
desired output of the network.
Yj
1.0
0.8
0.6
0.4
0.2
Netj
-4
-2
2
4
Figure 9.**: The sigmoid function
Apart from the sigmoid function, there is also function that uses a treshold value such as in
Binary treshold function. This function will limit the Yj value to 0 or 1 depending on the
value of Netj relative to a particular treshold value, . This is illustrated in Figure 9.***
below.
Yj
1.0
Yj =
1
if Netj  
0
if Netj < 
Netj
Figure 9.6: The Binary treshold function
The process in the activation function can take place at the output on each neuron, or
done only at the output layer, depending on how we model our network. Next, we will see
an example of a network that learns the exclusive-or (XOR) arithmetic.
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EXAMPLE 9.2
This example will show us a network that learns the XOR relationship. Table 9.1 shows the
XOR operator's truth table:
Table 9.1: XOR relationship
INPUT
DESIRED
OUTPUT
X1
X2
1
1
0
1
0
1
0
1
1
0
0
0
The output can only be true if either x1 or x2 is true (1). The network is designed to be a
layered feedfoward network, with 2 input units, one hidden layer with 2 hidden units and an
output unit. Feedforward means that the connection of each neuron will only be in one
direction.
Exercise *** CE BUAT LATIHAN PENGIRAAN
1.
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Give three examples of Groupware that support GDSS and describe their
criteria briefly.
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
A learning rule to adapt the weights
There are many learning algorithms in ANN used according to the required tasks. There
are two groups of learning algorithm in ANN; supervised and unsupervised learning
illustrated in Figure 9.***, depicted from [L. Medsker and J. Liebowitz, 1994].
Learning Algorithm
Discrete/Binary input
Supervised
Simple Hopfield
Outerproduct AM
Hamming net
Continuous input
Unsupervised
ART-1
Carpenter/
Grossberg
Supervised
Delta rule
Gradient descent
Competitive
learning
Neocognitron
Preceptor
Unsupervised
ART-3
SOFM
Clustering
algorithm
Architecture
Supervised
Recurrent
Hopfield
Unsupervised
Feedforward
Estimators
Nonlinear vs
Linear
Backpropagation
ML perceptron
Boltzman
SOFM
Extractors
ART-1ART-2
Figure 9.**: Learning Algorithms And Architecture In Neural Network
As discussed before, supervised learning involves a learning process from a given set of
data of which the final outcomes are known. For example a set of historical data on
diagnosing heart attack is presented to a network as an input. The desired output should
be equal to 1 if the patient had a heart attack and 0 if the patient is healthy. The network
will work on the data and produce the actual output. It will learn to produce output as
closed as the desired output by calculating the difference between the two outputs.
Another version of this technique is to consider the previous weight change when
attempting to do error correction as in the backpropagation and the Hopfield network.
Meanwhile, in unsupervised learning, the network is left to learn on its own with no desired
output (self-organized). The network will cluster particular patterns of output that have
something in common. For example, the network is trained to recognize jockey players
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and sumo players, which obviously use the weight parameter in clustering. Therefore, the
network must able to group data that shared relatively similar weights. This kind of selflearning can be seen in Adaptive Resonance Theory (ART) and Self-organizing Feature
Maps (SOFM).
Generally, supervised learning process in ANN involves processes of:
a. Computing the output from input layers, Yj
b. Comparing Yj with the desired output, Z.
c. Adjusting the weight and repeat (a) and (b) to minimize difference , between Yj
and Z
Contoh back propagation
example
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The main goal of learning process is to decrease the value of  so that Yj is as closed to Z
as possible by adjusting the weights.
9.4.2 Example
ANN has been used in many applications such as in credit card
Now we will discuss examples on the usage ANN in decision support. We first start with a
very simple example that is to calculate
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9.4.3 Example of calculation
Exercise 3.4
1.
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9.5 OTHER TECHNIQUES IN AI
Exercise 3.5
1.
Explain how GDSS concept can be applied in education sector.
SUMMARY
GDSS concept, supported by its technologies can be applied in group decision-making.
GDSS technology is meant to enhance group performance and decision-making and less
time consuming. There are four type of collaboration that helps in decision-making. The
decision process can be in a meeting room, where all members can meet at the same
time. It can also occur among geographically distributed members. This can be done by
using various GDSS Groupware. GDSS concept can be applied not only in the
organization, but also in education.
TEST 1
Instruction: Answer all questions in exactly 15 minutes.
1. Describe four types of collaboration.
2. Give 3 examples of group supporting tools.
3. When would you use various group support tools?
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4. How can videoconferencing and groupware helps in an organization that has few
branches?
5. What is a definition for a "Virtual Organization"?
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TEST 2
Instruction: Answer all questions in exactly 30 minutes.
1.
2.
3.
4.
5.
In what condition do managers need the support of GDSS?
What advantage can managers get from the support provided by GDSS?
What type of technologies used in order to make a “Virtual Workplace” possible?
What is a definition for a "Virtual Organization"?
What is the advantage and disadvantage working in Virtual Organization?
Reference
L. Medsker and J. Liebowitz, Design and Development of Expert System and Neural
Computing, New York: Macmillan, 1994.
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