assignment 6 aplication of artificial intelligent

8/17/2019 Assignment 6 Aplication of Artificial Intelligent

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Department of Civil Engineering \ Transportation

Faculty of Engineering & Built Environment

The National University of Malaysia

Semester II - Session 2015/2016

Task 6 \ Application of Artificial intelligent

Urban Traffic Management System

Name Student

Ameer Abdul Adheem Hussein

P81466

Lecturer

PROF. DATO' IR. DR RIZA ATIQ ABDULLAH BIN

O.K. RAHMAT

Dr. MUHAMAD NAZRI BIN BORHAN


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Task 6:

Artificial intelligent is widely used in any smart system. Explain the following

components of artificial intelligent and how they can be used in an intelligent

transport system:

1. Neural Network.2. Genetic Algorithm.

3. Expert System.

4. Fuzzy Logic.

Solution \\

Introduction: Artificial Intelligence is a branch of Science which deals with helping machines

finds 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 behavior appears , Artificial

Intelligent is generally associated with Computer Science, but it has manyimportant links with other fields such as Math's, 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 mechanicalcomputations, 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-leveldeliberative 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.


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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 ofmachine 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.

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 influential 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 andreliably. 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.


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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 ashospitals, 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 personal. So if you know what

you're doing, there's plenty of money to be made from interested big

companies.


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1- 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:

A neural network acquires knowledge through learning.

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 learnthese 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.


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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 thesoftware 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 uses a neural network as the classification engine.


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The 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 individual's 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.


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2- 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 has 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.


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3- 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).

a) 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 knowledgebase 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.

The non-expert user queries the expert system. This is done by asking a

question, or by answering questions asked by the expert system.

b) B - 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 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).


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Helping to diagnose car engine problems (like medical diagnosis, but for

cars).

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).

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.

Some Problems of expert system:

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).


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4- 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.

The figure above 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 1000 vehicles per hour (vph) fully belong to the

medium traffic level set. Traffic volumes less than 400 vph or more than 1400

vph would not be regarded as medium at all (membership function value = 0).

However, values between 400 and 800 vph, or between 1000 and 1400 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 1000 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 problemobjectives or input values, but rather provides a standardized way to

systematically capture and define ambiguity.


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Topics of Interest:

The AITS Thematic Track welcomes and encourages contributions reporting on

original research, work under development and experiments of different AI

techniques, such as neural networks, biologically inspired approaches,

evolutionary algorithms, knowledge-based and expert systems, case-basedreasoning, fuzzy logics, intelligent agents and multi-agent systems, support

vector regression, data mining and other pattern-recognition and optimization

techniques, as well as concepts such as ambient intelligence and ubiquitous

computing, service-oriented architectures, and ontology, to address specific

issues in contemporary transportation, which would include (but are not limited

to):

Different modes of transport and their interactions. Intelligent and real-time traffic management and control.

Design, operation, time-tabling and management of logistics systems and

freight transport.

Transport policy, planning, design and management.

Environmental issues, road pricing, security and safety.

Transport systems operation. Application and management of new technologies in transport.

Travel demand analysis, prediction and transport marketing.

Traveler information systems and services.

Ubiquitous transport technologies and ambient intelligence.

Pedestrian and crowd simulation and analysis.

Urban planning toward sustainable mobility.

Service oriented architectures for vehicle-to-vehicle and vehicle-to-

infrastructure communications.

Assessment and evaluation of intelligent transportation technologies.

Human factors in intelligent vehicles.

Autonomous driving.

Artificial transportation systems and simulation.

Surveillance and monitoring systems for transportation and pedestrians.


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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 intransportation 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 universalfunction 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.

Fourth & 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.

assignment 6 aplication of artificial intelligent

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