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
Students learn about: Students learn to: Teaching and learning strategies Resources
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
• Comprehension & class discussion
• Activity – Use the Internet to
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
Students learn about: Students learn to: Teaching and learning strategies Resources
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
• Comprehension & class discussion
• 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)
Students learn about: Students learn to: Teaching and learning strategies Resources
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
• defining & analysing the problem
(Core 1)
• designing possible solutions (Core 1)
• evaluation criteria (Core 1)
• management (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
Students learn about: Students learn to: Teaching and learning strategies Resources
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
• Comprehension & class discussion
(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
Students learn about: Students learn to: Teaching and learning strategies Resources
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
• Comprehension & class discussion
• Activity – Case Study
• Activity – Use the Internet to
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)
Students learn about: Students learn to: Teaching and learning strategies Resources
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
• Comprehension & class discussion
• Activity – Explore case study &
activities in textbook.
• Grover (2008), p. 99-102, Ch 8.6
Students learn about: Students learn to: Teaching and learning strategies Resources
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)
• defining & analysing the problem
(Core 1)
• designing possible solutions (Core 1)
• evaluation criteria (Core 1)
• management (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
• Comprehension & class discussion
• Activities
• Grover (2008), p. 102-105, Ch 8.7
• Powers (2004), p. 182-186, Ch 8.5
• Activity 7 diceSimulation
(from Powers (2004) CD-ROM)
• Activity 9 excursion
(from Powers (2004) CD-ROM)
• Activity 10 saving (optional content)
(from Powers (2004) CD-ROM)
Students learn about: Students learn to: Teaching and learning strategies Resources
Minor Project (Lesson Duration: 4 - 5 Classes)
• spreadsheet design (Option 1)
• simulation software (Option 1)
• defining & analysing the problem
(Core 1)
• designing possible solutions (Core 1)
• evaluation criteria (Core 1)
• management (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
(from Powers (2004) CD-ROM)
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
for i := 1 to 100 do
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
for i := 1 to 100 do
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
for i := 1 to 100 do
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
for i := 1 to 100 do
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
for i := 1 to 100 do
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;
for i := 1 to 100 do
writeln();
writeln('You should try ', conclusion);
readln();
end.