EMT443 Ass3

EMT443 – Assignment 3

Project-based Unit of Work Option 1 – Artificial Intelligence, Simulation & Modelling - Program for Stage 5, 200-Hour IST Course

Corey Taskis - 11546710

Stage 5 Program for 200-Hour Course

Year 9

Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10

Term 1 Option 5 - The Internet & Website Development…

Term 2 … Opt. 5 Option 6 - Networking Systems…

Term 3 … Opt. 6 Opt. 3...

Term 4 … Option 3 - Database Systems Exams, Camps etc

Year 10

Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10

Term 1 Option 8 - Software Development & Programming

Term 2 … Opt. 8 Option 1 - Artificial Intelligence, Simulation & Modelling…

Term 3 … Opt. 1 Option 7 - Robotics & Automated Systems…

Term 4 … Opt. 7 Exams, Work Experience, etc

Yr 10 - Information and Software Technology

Unit 10.2

Artificial Intelligence, Simulation & Modelling

Unit Description

Students will explore the concept of Artificial Intelligence, Simulation and Modelling as a tool for guided/assisted decision making.

The unit will be delivered as follows:

Artificial Intelligence

- Definitions & Historical Perspective

- Areas & Requirements

- Project - Students will design a simple expert system

Simulation and Modelling

- Definitions & Requirements

- Purposes & Advantages/Limitations

- Project – Students will design a spreadsheet based Modelling tool

Content

Focus areas:

Core Content

- Design, Produce and Evaluate

Options

- Artificial Intelligence, Simulation & Modelling

Contributing Areas:

Core Content

- Past, Current and Emerging Technologies

- Hardware

- Software

Options

- Software Development and Programming

Outcomes

A student:

5.1.1 – Selects and justifies the application of appropriate software programs to a range of tasks

5.2.1 – Describes and applies problem solving processes when creating solutions

5.2.2 – Designs, produces and evaluates appropriate solutions to a range of challenging problems

5.2.3 – Critically analyses decision making processes in a range of information and software solutions

5.4.1 – Analyses the effects of past, current and emerging information and software technologies on the individual and

society

5.5.2 – Communicates ideas, processes and solutions to a targeted audience

Assessment

Knowledge and skills covered in this topic are formally

assessed through tasks including:

- Project work

- Assessable tasks

- Short Tests

Resources

- Internet

- Hardware

- PCs and network drives

- Software

- Microsoft Word

- Microsoft Excel

- ES-Builder 3.0

- Programming Language (Pascal or Visual Basic, etc)

- Text Books

- Grover, D., Range, J., Knights, H., Gormley, E. & Perri, S. (2008). Information and software technology. Melbourne: Pearson

Education Australia.

- Powers, G. (2004). Hi tech: Information and software technology. Melbourne: Heinemann.

Suggested Unit Length: 8 weeks (~32 lessons)

Students learn about: Students learn to: Teaching and learning strategies Resources

Artificial Intelligence (Lesson Duration: 2 - 3 Classes)

• definition of intelligence and

artificial intelligence (Option 1)

• purpose of artificial intelligence

(Option 1)

• define and describe artificial

intelligence

• Comprehension & class discussion

• Activity 1

• Activity – Interact with a ‘Chat bot’.

How human do they seem? Would

they pass the Turing Test?

• Activity – Use the Internet to

research the the future direction of

Artificial Intelligence.

• Grover (2008), p. 84, Ch 8.1

• Appendix A & B

• historical perspective of artificial

intelligence (Option 1)

• the impact of past and current

technologies (Core 2)

• investigate the work of pioneers of

artificial intelligence, for example

Alan Turing

• Grover (2008), p. 84, Ch 8.1

• Powers (2004), p. 167, Ch 8.1

• Activity 1 timeline

• http://en.wikipedia.org/wiki/Chatterbot

• Chat Bots examples:

- A.L.I.C.E.

http://alice.pandorabots.com/

- Cleverbot

http://www.cleverbot.com/

- Jabberwacky

http://www.jabberwacky.com/

• artificial intelligence in movies

(optional content)

• recognise fictional representations

of artificial intelligence

• http:/homepages.inf.ed.ac.uk/

rbf/AIMOVIES/AImovies.htm

• future of artificial intelligence

• the impact of current & emerging

technologies (Core 2)

• discover some future fields of

research into artificial intelligence


Requirements of Artificial Intelligence (Lesson Duration: 1 - 2 Classes) **NOTE** - This lesson contains elements of Cooperative Learning, indicated by (C)

• software (Option 1), (Core 7)

• hardware (Option 1), (Core 4)

• research the requirements of

artificial intelligence for a range of

situations



research the Hardware and Software

requirements of 2 specific Artificial

Intelligence systems. (C) – Group activity. Direct students

to work in co-operative groups

• Grover (2008), p. 90-95, Ch 8.3

• Powers (2004), p. 168-169, Ch 8.1


Areas of Artificial Intelligence (Lesson Duration: 6 - 8 Classes) **NOTE** - This lesson contains elements of Scaffolded Instruction, indicated by (S)

• intelligent systems (Option 1)

• knowledge bases (Option 1)

• daemons (Option 1)

• agents (Option 1)

• identify a range of intelligent

systems including games

• explore and contrast the uses for

daemons, agents and knowledge

bases


• Grover (2008), p. 85-89, Ch 8.2

• Powers (2004), p. 170-173, Ch 8.2

• neural networks (Option 1) • explore and contrast the uses for

daemons, agents, expert systems,

neural networks and knowledge

bases

• Video Clip & Activity 2

(S) – Teacher directed activity

• Textbooks as above

• Appendix C

• Teachers Resource & Video Link: (Activity 2 asimo -teacher)

• Activity 2 asimo)

• expert systems (Option 1)

• defining & analysing the problem

(Core 1)

• designing possible solutions (Core 1)

• evaluation criteria (Core 1)

• management (Core 1)

• interface design (Core 7)

• examine a range of expert systems

• investigate the creation of an expert

system shell for a particular purpose

• Activities – preparation for Major

Project

(S) – Tutorial directed activity

• Textbooks as above

• Activity 3 ES tutorial

• Activity 4 ES1

• Activity 5 ES2

• Activity 4b

• PASCAL example for Activity 4b:

(Appendix D)


Major Project (Lesson Duration: 6 Classes)

• expert systems (Option 1) Algorithms (Option 8)

• definitions and descriptions

• representing algorithms

• examples such as recipes, directions,

appliance instructions

Programming language (Option 8)

• function of programming language

• examples of a programming

language

Testing (Option 8)

• test data

• boundaries


(Core 1)




• interface design (Core 7)

• investigate the creation of an expert

system shell for a particular purpose

• represent algorithms by using either

flowchart or pseudocode

• convert algorithms into basic code

using a given language syntax

• test programming code using test

data to check for the desired

outcome

• Expert Systems Project

(Pre-requisite tasks completed

during previous lesson)

• Activity 6 ES3 Project


Modelling & Simulation (Lesson Duration: 1 - 2 Classes) **NOTE** - This lesson contains elements of Scaffolded Instruction, indicated by (S)

• definition of a model and a

simulation (Option 1)

• define and describe models and

simulations


(S) – Teacher directed discussion

• Grover (2008), p. 95-97, Ch 8.4

• Powers (2004), p. 175-177, Ch 8.3

• purposes of models and simulations

(Option 1)

• investigate the purposes for models

and simulations in a range of

situations


Requirements of Modelling & Simulation (Lesson Duration: 3 - 4 Classes) **NOTE** - This lesson contains elements of Cooperative Learning, indicated by (C)

• hardware needs such as speed,

storage (Option 1), (Core 4)

• simulators such as flight, driving

• software requirements including

languages (Option 1), (Core 7)

• examine the hardware needs for

operating simulation programs

• explore a range of simulations

• identify software requirements for

models and simulations


• Activity – Case Study


research the Hardware and Software

requirements of 2 specific Simulation

systems.

(C) – Group activity. Direct students

to work in co-operative groups

• Activity – Experience the use of a PC

based simulator game.

• Grover (2008), p. 98-99, Ch 8.5

• Powers (2004), p. 178-181, Ch 8.4

• Activity 8 simulatorCaseStudy

(from Powers (2004) CD-ROM)


Advantages & limitations of models & simulation programs (Lesson Duration: 1 - 2 Classes)

• predictions such as global warming,

ozone layer changes (Option 1)

• trial situations such as weather

forecasting (Option 1)

• investigate the use of educational

simulations and games

• propose advantages and limitations

of simulation and modelling

programs

• investigate and evaluate predictions

and trial situations that used model

and simulation programs


• Activity – Explore case study &

activities in textbook.

• Grover (2008), p. 99-102, Ch 8.6


Using model & simulation programs (Lesson Duration: 4 - 5 Classes)

• variables to ensure accuracy

(Option 1)

• spreadsheets (Option 1)

• what-if predictions for spreadsheets

such as goal seek and look ups

(Option 1)


(Core 1)




• examine a range of simulation

programs and describe how variables

are adjusted to ensure accuracy

• use spreadsheets to make

predictions

• goal seek and look ups

• critically analyse the effectiveness of

• spreadsheets when solving


• Activities

• Grover (2008), p. 102-105, Ch 8.7

• Powers (2004), p. 182-186, Ch 8.5

• Activity 7 diceSimulation


• Activity 9 excursion


• Activity 10 saving (optional content)



Minor Project (Lesson Duration: 4 - 5 Classes)

• spreadsheet design (Option 1)

• simulation software (Option 1)


(Core 1)




• design, produce and evaluate a

predictive spreadsheet including

macros for a specified situation

• examine and explain the operation

of selected simulation software

• Modelling & Simulation Project • Activity 11 goldcoastConference


APPENDIX (A)

Source: http://www.iscid.org/encyclopedia/Artificial_Intelligence

Overview of Artificial Intelligence

Artificial Intelligence, otherwise known as AI, is the study and development of intelligent machines

capable of performing complex tasks that require thought and behavior normally associated with

human intelligence. Computer programs are a common area of specialization in this branch of

science. Artificial Intelligence adapts characteristics of human problem-solving skills and then applies

them as algorithms easily comprehended by computer systems. Such systems are routinely and

widely used today by hospitals, corporations, militaries and homes around the world.

The very nature of computers allows them to easily and consistently perform simple, repetitive tasks

by utilizing fixed program rules. In itself, this is an essential and valuable characteristic, relieving

people of tremendous amounts of tedious computation.

The challenge, however, is for researchers and developers of Artificial Intelligence to push

boundaries by elevating the capabilities of computer systems so as to be adaptable and creative

when handling specific and unfamiliar situations. To produce machines that are capable of

automating even the most human of tasks requiring intelligent thought. The purpose of Artificial

Intelligence research should not be misunderstood, though. It is not to replicate human beings, but

rather to develop useful machines that can solve problems as well as humans. To such an end,

researchers may employ methods that perform more computations than commonly achievable by

human endeavor or methods that are not observed in people.

There is an ongoing philosophical debate about the nature of AI. All successful applications of

artificially intelligent systems to date have been highly specific in their abilities. Some AI systems can

play chess very well. Some do a remarkable job at organizing information. Others play as human-like

opponents in video games. Still, very few people view these successes as proof that computers can

think. Yet some do. The major philosophical break on the nature of AI systems is between Strong

and Weak views. Strong Artificial Intelligence is the view that computers either will be or are capable

of thinking. Weak Artificial Intelligence is the view that computers are perfectly good and simulating

intelligent abilities, but that there is no thinking (especially, no conscious thinking) going on inside AI

systems. In these kinds of philosophical disputes, the point of disagreement often turns out to be

definitional. What exactly constitutes "thinking"? What sort of general problem solving abilities

would be necessary to say that a thing thinks?

APPENDIX (B)

Source: http://www.iscid.org/encyclopedia/Applications_of_Artificial_Intelligence

Applications of Artificial Intelligence

The applications of Artificial Intelligence are abundant and widespread, especially in developed

countries. In fact, Artificial Intelligence has become such a mainstay in today’s world that it is taken

for granted by the majority of people who benefit from its efficiency. Air conditioners, cameras,

video games, medical equipment, traffic lights, refrigerators: all function by way of developments in

“smart” technology or fuzzy logic. Large financial and insurance institutions rely heavily on Artificial

Intelligence to process the huge quantities of information that are fundamental to their business

practices.

The application of computer speech recognition, though more limited in utilization and practical

convenience, has made it possible to interact with computers by using speech instead of

writing. Robotics, the study and development of robots, is another common application whose end

goal can be anything from entertainment (such as robot pets), to research (such as Mars rovers), to

safety (such as fire detection and extinguishment). Natural language processing, a subfield of

Artificial Intelligence, provides computers with the understanding they require to handle

information being encoded by humans. Computer vision instructs computers on how to comprehend

images and scenes. It has as some of its goals: image recognition, image tracking and image

mapping. This application is valued in the fields of medicine, security, surveillance, military

operations, even movie-making.

APPENDIX (C)

Neural Networks

Artificial Neural Networks (ANN) are Intelligent systems that are designed to solve problems in a

manner that imitates the way the human brain works. Biological neural networks are thousands of

times more complicated than the mathematical models used for current ANNs.

The simplest definition of an ANN is that it is a network of many simple, interconnected processors.

Processors in an ANN often record a small amount of specific memory locally.

Each processor can be reached by many different inputs. The processor acts on the data, and then

delivers its output as an input for the next processor.

Through multiple examples, or ‘training’, the ANN will learn to recognise patterns of data. This

means that the ANN will begin to learn the relationship between an input and the appropriate

output.

For example, a young child learns the difference between a cat and a dog by seeing many different

cats and dogs, and being told which is which. If the input is an image of a cat, the child’s neural

network will send that input to the group of processors involved in identifying the image. As the

neural network begins to learn the features of the input, it remembers the pattern, or the path

taken through the processors, to arrive at the correct output.

APPENDIX (D)

Expert Systems – Programming Solution

This is a solution example for Activity 4b, written in the PASCAL programming language.

program ES;

var i, answer : integer;

conclusion : string;

begin

for i := 1 to 100 do {clear screen loop}

writeln();

writeln('Choose Adventure Type: ');

writeln();

writeln('Air : 1');

writeln('Water : 2');

writeln('Land : 3');

write('> ');

readln(answer);

case answer of

1 : begin

for i := 1 to 100 do

writeln();

writeln('Choose Duration of Air Adventure:');

writeln();

writeln('Fast & Furious : 1');

writeln('Extended Experience : 2');

write('> ');

readln(answer);

if answer = 1 then conclusion := ('Sky Diving')

else if answer = 2 then conclusion := ('Hang Gliding')

end;

2 : begin


writeln();

writeln('Choose Type of Water Adventure:');

writeln();

writeln('Underwater : 1');

writeln('River Rapids : 2');

writeln('Waves : 3');

writeln('Calm Water : 4');

write('> ');

readln(answer);

case answer of

1 : conclusion := ('Scuba Diving');

2 : conclusion := ('White Water Rafting');

3 : conclusion := ('Surfing');

4 : conclusion := ('Water Skiing');

end;

end;

3 : begin


writeln();

writeln('Choose type of Land Adventure:');

writeln();

writeln('Snow : 1');

writeln('High Places : 2');

writeln('Underground : 3');

writeln('Shooting : 4');

writeln('Bush : 5');

write('> ');

readln(answer);

case answer of

1 : begin


writeln();

writeln('Choose amount of Snow Equipment Required');

writeln();

writeln('More : 1');

writeln('Less : 2');

write('> ');

readln(answer);

if answer = 1 then conclusion := ('Bob Sleigh')

else if answer = 2 then conclusion := ('Snow Boarding')

end;

2 : begin


writeln();

writeln('Choose Level of Control Required');

writeln();

writeln('Free Falling : 1');

writeln('Tethered Falling : 2');

writeln('Not Falling : 3');

write('> ');

readln(answer);

if answer = 1 then conclusion := ('BASE Jumping')

else if answer = 2 then conclusion := ('Bungee Jumping')

else if answer = 3 then conclusion := ('Rock Climbing')

end;

3 : conclusion := ('Caving');

4 : begin


writeln();

writeln('Choose Type of Target');

writeln();

writeln('Moving : 1');

writeln('Stationary : 2');

write('> ');

readln(answer);

if answer = 1 then conclusion := ('Paintballing')

else if answer = 2 then conclusion := ('Target Shooting')

end;

5 : conclusion := ('Mountain Biking');

end;

end;

end;


writeln();

writeln('You should try ', conclusion);

readln();

end.

Documents

EMT443 Ass3