This document provides an overview of artificial intelligence and several AI techniques. It discusses neural networks, genetic algorithms, expert systems, fuzzy logic, and the suitability of AI for solving transportation problems. Neural networks can be used to perform tasks like optical character recognition by analyzing images. Genetic algorithms use principles of natural selection to arrive at optimal solutions. Expert systems mimic human experts to provide advice. Fuzzy logic allows for gradual membership in sets rather than binary membership. Complexity and uncertainty make transportation well-suited for AI approaches.

1. FACULTY OF ENGINEERING
DEPARTMENT OF CIVIL AND STRUCTURAL ENGINEERING
KKKA6424
INTELLIGENT URBAN TRAFFIC CONTROL SYSTEM
Prof. Dr. Riza Atiq Abdullah O.K. Rahmat
TASK (6) ARTIFICIAL INTELLIGENT
PREPARED BY:
WAEL SAAD HAMEEDI
P71062

2. INTRODUCTION TO ARTIFICIAL INTELLIGENT
Definition...
Artificial Intelligence is a branch of Science which deals with helping machines find solutions
to complex problems in a more human-like fashion. This generally involves borrowing
characteristics from human intelligence, and applying them as algorithms in a computer
friendly way. A more or less flexible or efficient approach can be taken depending on the
requirements established, which influences how artificial the intelligent behaviour appears.
AI is generally associated with Computer Science, but it has many important links with other
fields such as Maths, Psychology, Cognition, Biology and Philosophy, among many others. Our
ability to combine knowledge from all these fields will ultimately benefit our progress in the
quest of creating an intelligent artificial being.
Motivation...
Computers are fundamentally well suited to performing mechanical computations, using
fixed programmed rules. This allows artificial machines to perform simple monotonous
tasks efficiently and reliably, which humans are ill-suited to. For more complex problems,
things get more difficult... Unlike humans, computers have trouble understanding specific
situations, and adapting to new situations. Artificial Intelligence aims to improve machine
behavior in tackling such complex tasks.
Together with this, much of AI research is allowing us to understand our intelligent
behavior. Humans have an interesting approach to problem-solving, based on abstract
thought, high-level deliberative reasoning and pattern recognition. Artificial Intelligence can
help us understand this process by recreating it, then potentially enabling us to enhance it
beyond our current capabilities.
Limitations...
To date, all the traits of human intelligence have not been captured and applied together to
spawn an intelligent artificial creature. Currently, Artificial Intelligence rather seems to
focus on lucrative domain specific applications, which do not necessarily require the full
extent of AI capabilities. This limit of machine intelligence is known to researchers as
narrow intelligence.
There is little doubt among the community that artificial machines will be capable of
intelligent thought in the near future. It's just a question of what and when... The machines
may be pure silicon, quantum computers or hybrid combinations of manufactured
components and neural tissue. As for the date, expect great things to happen within this
century!

3. Technology...
There are many different approaches to Artificial Intelligence, none of which are either
completely right or wrong. Some are obviously more suited than others in some cases, but
any working alternative can be defended. Over the years, trends have emerged based on the
state of mind of influencial researchers, funding opportunities as well as available computer
hardware.
Over the past five decades, AI research has mostly been focusing on solving specific
problems. Numerous solutions have been devised and improved to do so efficiently and
reliably. This explains why the field of Artificial Intelligence is split into many branches,
ranging from Pattern Recognition to Artificial Life, including Evolutionary
Computation and Planning.
Applications...
The potential applications of Artificial Intelligence are abundant. They stretch from
the military for autonomous control and target identification, to the entertainment industry for
computer games and robotic pets. Let's also not forget big establishments dealing with huge
amounts of information such as hospitals, banks and insurances, who can use AI to predict
customer behavior and detect trends.
As you may expect, the business of Artificial Intelligence is becoming one of the major
driving forces for research. With an ever growing market to satisfy, there's plenty of room for
more personel. So if you know what you're doing, there's plenty of money to be made from
interested big companies!

4. WHAT IS A NEURAL NETWORK ?
A neural network is a powerful data modeling tool that is able to capture and represent
complex input/output relationships. The motivation for the development of neural network
technology stemmed from the desire to develop an artificial system that could perform
"intelligent" tasks similar to those performed by the human brain. Neural networks resemble
the human brain in the following two ways:
1. A neural network acquires knowledge through learning.
2. A neural network's knowledge is stored within inter-neuron connection strengths known
as synaptic weights.
The true power and advantage of neural networks lies in their ability to represent both
linear and non-linear relationships and in their ability to learn these relationships directly
from the data being modeled. Traditional linear models are simply inadequate when it
comes to modeling data that contains non-linear characteristics.
The most common neural network model is the multilayer perceptron (MLP). This type of
neural network is known as a supervised network because it requires a desired output in
order to learn. The goal of this type of network is to create a model that correctly maps the
input to the output using historical data so that the model can then be used to produce the
output when the desired output is unknown. A graphical representation of an MLP is
shown below.
Block diagram of a two hidden layer multiplayer perceptron (MLP). The inputs are fed into the input
layer and get multiplied by interconnection weights as they are passed from the input layer to the first
hidden layer. Within the first hidden layer, they get summed then processed by a nonlinear function
(usually the hyperbolic tangent). As the processed data leaves the first hidden layer, again it gets
multiplied by interconnection weights, then summed and processed by the second hidden layer. Finally
the data is multiplied by interconnection weights then processed one last time within the output layer to
produce the neural network output.

5. A good way to introduce the topic is to take a look at a typical application of neural
networks. Many of today's document scanners for the PC come with software that performs
a task known as optical character recognition (OCR). OCR software allows you to scan in a
printed document and then convert the scanned image into to an electronic text format
such as a Word document, enabling you to manipulate the text. In order to perform this
conversion the software must analyze each group of pixels (0's and 1's) that form a letter and
produce a value that corresponds to that letter. Some of the OCR software on the market
use a neural network as the classification engine.
Demonstration of a neural network used within an optical character recognition (OCR) application.
The original document is scanned into the computer and saved as an image. The OCR software breaks
the image into sub-images, each containing a single character. The sub-images are then translated from
an image format into a binary format, where each 0 and 1 represents an individual pixel of the sub-
image. The binary data is then fed into a neural network that has been trained to make the association
between the character image data and a numeric value that corresponds to the character. The output
from the neural network is then translated into ASCII text and saved as a file.

6. Of course character recognition is not the only problem that neural networks can solve.
Neural networks have been successfully applied to broad spectrum of data-intensive
applications, such as:
 Process Modeling and Control - Creating a neural network model for a physical
plant then using that model to determine the best control settings for the plant.
 Machine Diagnostics - Detect when a machine has failed so that the system can
automatically shut down the machine when this occurs.
 Portfolio Management - Allocate the assets in a portfolio in a way that maximizes
return and minimizes risk.
 Target Recognition - Military application which uses video and/or infrared image
data to determine if an enemy target is present.
 Medical Diagnosis - Assisting doctors with their diagnosis by analyzing the reported
symptoms and/or image data such as MRIs or X-rays.
 Credit Rating - Automatically assigning a company's or individuals credit rating based
on their financial condition.
 Targeted Marketing - Finding the set of demographics which have the highest
response rate for a particular marketing campaign.
 Voice Recognition - Transcribing spoken words into ASCII text.
 Financial Forecasting - Using the historical data of a security to predict the future
movement of that security.
 Quality Control - Attaching a camera or sensor to the end of a production process to
automatically inspect for defects.
 Intelligent Searching - An internet search engine that provides the most relevant
content and banner ads based on the users' past behavior.
 Fraud Detection - Detect fraudulent credit card transactions and automatically
decline the charge.
NeuroSolutions is a leading edge neural network development software that combines a
modular, icon-based network design interface with an implementation of advanced learning
procedures, such as Levenberg-Marquardt and backpropagation through time. Some other
notable features include C++ source code generation, customized components through
DLLs, neuro-fuzzy architectures, and programmatic control from Visual Basic using OLE
Automation.

7. WHAT IS GENETIC ALGORITHMS ?
Genetic algorithms (GAs) are stochastic algorithms whose search methods are based on the
principle of survival of the fittest. In recent years, GAs have been applied to a wide range of
difficult optimization problems for which classical mathematical programming solute
approaches were not appropriate. The basic idea behind GAs is quite simple. The procedure
starts with a randomly generated initial population of individuals, where each individual or
chromosome represents a potential solution to the problem under consideration. Each
solution is evaluated to give some measure of its “fitness.” A new population is then formed
by selecting the more fit individuals. Some members of this new population undergo
alterations by means of genetic operations (typically referred to as crossover and mutation
operations) to form new solutions. This process of evaluation, selection, and alteration is
repeated for a number of iterations (generations in GA terminology). After some number of
generations, it is expected that the algorithm “converges” to a near-optimum solution.
In addition to the aforementioned AI methods, there has recently been an interest in a new
modeling paradigm known as agent-based modeling (ABM). This modeling approach came
out of research work in AI as well as in complex systems analysis. The idea behind ABM is
to describe a system from the perspective of its constituent units. The approach is therefore
quite appropriate for modeling complex systems whose behavior emerges as a result of
interactions among the components making up the system. Since transportation systems
exhibit almost all the characteristics of complex systems, ABM has been attracting a lot of
attention within the transportation research community. Given this, ABM will be discussed
in the last section of this circular.

8. WHAT IS AN EXPERT SYSTEM ?
An expert system is computer software that attempts to act like a human expert on a
particular subject area.
Expert systems are often used to advise non-experts in situations where a human expert in
unavailable (for example it may be too expensive to employ a human expert, or it might be a
difficult to reach location).
How Do Expert Systems Work?
An expert system is made up of three parts:
 A user interface - This is the system that allows a non-expert user to query (question)
the expert system, and to receive advice. The user-interface is designed to be
a simple to use as possible.
 A knowledge base - This is a collection of facts and rules. The knowledge base is
created from information provided by human experts
 An inference engine - This acts rather like a search engine, examining the knowledge
base for information that matches the user's query

9. The non-expert user queries the expert system. This is done by asking a question, or
by answering questions asked by the expert system.
The inference engine uses the query to search the knowledge baseand then provides an
answer or some advice to the user.
Where Are Expert Systems Used?
Medical diagnosis (the knowledge base would contain medical information, the symptoms
of the patient would be used as the query, and the advice would be a diagnose of the
patient’s illness)
Playing strategy games like chess against a computer (the knowledge base would contain
strategies and moves, the player's moves would be used as the query, and the output would
be the computer's 'expert' moves)
Providing financial advice - whether to invest in a business, etc. (the knowledge base would
contain data about the performance of financial markets and businesses in the past)
Helping to identify items such as plants / animals / rocks / etc. (the knowledge base would
contain characteristics of every item, the details of an unknown item would be used as the
query, and the advice would be a likely identification)
Helping to discover locations to drill for water / oil (the knowledge base would contain
characteristics of likely rock formations where oil / water could be found, the details of a
particular location would be used as the query, and the advice would be the likelihood of
finding oil / water there)
Helping to diagnose car engine problems (like medical diagnosis, but for cars!)
Can Expert Systems Make Mistakes?
Human experts make mistakes all the time (people forget things, etc.) so you might imagine
that a computer-based expert system would be much better to have around.
However expert systems can some problems:
 Can't easily adapt to new circumstances (e.g. if they are presented with totally
unexpected data, they are unable to process it)
 Can be difficult to use (if the non-expert user makes mistakes when using the system,
the resulting advice could be very wrong)
 They have no 'common sense' (a human user tends to notice obvious errors, whereas
a computer wouldn't)

10. WHAT IS A FUZZY LOGIC ?
Fuzzy set theory was proposed by Zadeh (1965) as a way to deal with the ambiguity
associated with almost all real-world problems. Fuzzy set membership functions provide a
way to show that an object can partially belong to a group. Classic set theory defines sharp
boundaries between sets, which mean that an object can only be a member or a nonmember
of a given set. Fuzzy membership functions allow for gradual transitions between sets and
varying degrees of membership for objects within sets. Complete membership in a fuzzy
function is indicated by a value of +1, while complete non-membership is shown by a value
of 0. Partial membership is represented by a value between 0 and +1.
Figure 1 shows an example of a fuzzy membership function defined for the set of “medium
traffic volume” on a certain highway. In this example, traffic volumes between 800 and
1,000 vehicles per hour (vph) fully belong to the medium traffic level set. Traffic volumes
less than 400 vph or more than 1,400 vph would not be regarded as medium at all
(membership function value = 0). However, values between 400 and 800 vph, or between
10,00 and 1,400 vph
would have partial membership in the medium traffic level set. In a crisp set definition, on
the other hand, only values between 800 and 1,000 vph would be regarded as medium,
while all other values would not (for example, a traffic volume of 799 vph would not be
regarded as a medium traffic level). The use of fuzzy set theory does not necessarily minimize
uncertainty related to problem objectives or input values, but rather provides a standardized
way to systematically capture and define ambiguity.

11. WHAT MAKES ARTIFICIAL INTELLIGENCE APPROPRIATE FOR
TRANSPORTATION PROBLEMS ?
Transportation problems exhibit a number of characteristics that make them amenable to
solution using AI techniques. First, transportation problems often involve both quantitative
as well as qualitative data. The fact that we often have to deal with qualitative data in
transportation makes the use of expert and FS an obvious choice. Second, in transportation
we often deal with systems whose behavior is very hard to model with traditional approach,
either because the interactions among the different system components are not fully
understood or because one is dealing with a lot of uncertainty stemming from the human
component of the system. For such complex systems, building empirical models, based on
observed data are, may be the only option remaining. NNs, given their universal function
approximation capabilities, are perfect tools for building such models. Third, transportation
problems often lead to challenging optimization problems that are quite challenging to solve
using traditional mathematical programming techniques, either because the relationships
are hard to specify analytically or because of the size of the problem and its computational
intractability. For these problems, GAs may provide an alternative solution approach.
Finally, the complex nature of transportation systems and the fact Artificial Intelligence
Applications in Transportation 5 that their behavior emerges as a result of interactions
among the system components makes ABM techniques quite appropriate for study the
behavior of the system.

12. THANK YOU