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CAI104 Assessment 3 Brief Project Module 12 Page 1 of 5
Task Summary
In this assessment, you are to program an AI agent to solve a real-world, challenging case study. This
assessment is done individually and you are to submit programs and supporting documents. Please
refer to the Task Instructions for details on how to complete this task. This assessment is intended to
test:
• Your understanding of the theories covered in Module 1 to 12.
• Your ability to formulate and frame a simplified real-world problem for an AI problem
solving technique.
• Your ability to choose a suitable AI technique for the problem.
• Your ability to develop an AI problem solving technique in a modern programming language.
• Your ability to provide a document to discuss the potential applications and ethics of the AI
solution.
Context
You are to create a robot path planner that is able to find an optimal path to navigate an
environment and reach a target. By completing this assessment, you will show your skills on
leveraging the best AI methods to solve a simplified real-world problem.
The maze can be seen in the image below. It can be seen that there are 12 rows and 24 columns,
meaning there is a total of 288 blocks on the map. There are four different types of blocks in this
map as follows:
• Green: wall
• White: space (void)
• Red: initial position of the robot
• Blue: the target

ASSESSMENT 3: PROJECT BRIEF
Subject Code and Title CAI104
Assessment Assessment 3: Project
Individual/Group Individual
Length Project and supporting document (2000 words) (10% +-)
Submission Due by 11:55pm AEST Sunday end of Module 12
Weighting 40%
Total Marks 100 marks

AssignmentTutorOnline

CAI104 Assessment 3 Brief Project Module 12 Page 2 of 5
You can easily represent the entire maze as a 2D array with 0s and 1s:
{1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1},
{1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1},
{1,0,
-1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,1},
{1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,1},
{1,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,1,1,0,0,9,1,0,1},
{1,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0,1,0,1},
{1,0,1,1,1,1,1,1,1,1,0,0,0,1,0,0,0,1,0,0,0,1,0,1},
{1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,1},
{1,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,0,1,0,0,0,1,0,1},
{1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1},
{1,0,0,0,1,0,1,1,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1},
{1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1},
In this array, we use the following numbers to represent different types of blocks:
• 1: wall
• 0: space (void)
• -1: initial position of the robot
• 9: the target
In this assessment, you need to design and implement a robot path planner. Note your learning
facilitator will give you the type and name of the search or optimization algorithms that you have to
implement. The learning facilitator might ask you to work on a randomly generated maze as well.
You do not have to draw a maze like the picture above. You can simply mark the shortest path
obtained by your algorithm using for instance 2 (or any other numbers except 1, 0, -1, and 9) in the
2D array. If you draw the maze, however, it will be a lot easier to visualise the path and make sure
that it is the closest path to the target when you are testing your program.
By implementation, we mean to write the steps that should be taken to find the shortest path from
the initial position to the target as a pseudocode.

CAI104 Assessment 3 Brief Project Module 12 Page 3 of 5
After implementation, write a reflective report detailing the experience of the development process.
The report should be 2000 words (10%+-) in length and include the following sections:
• Overview
• What went right
• What went wrong
• What you are not sure about
• Conclusion
Note that your pseudocode is not be included in the word count.
Task Instructions
• Appropriate, effective and correct usage of pseudocode to design and develop and
algorithm
• Effective use of search and or optimization algorithms in AI.
• Good selection of search and/or optimization algorithms.
The pseudocode that you will be submitting:
1. Should be clear and detailed
2. Should be structured and written with the best practices.
3. There should be enough number of comments to show your understanding of the program.
4. Flowcharts can be used to complement the pseudocode
When you submit the electronic version of your project make sure to use the following names:
• Name the source code folder as: Source – Student Name
• Name the solution as: YourGameName.sln
Submission Instructions
Please submit an MS Word or a PDF file including your pseudocode and reflective report.
CAI104 Assessment 2 Brief Project Module 12 Page 4 of 5
Assessment Rubric

Assessment
Attributes
Fail
(Yet to achieve
minimum standard)
0-49%
Pass
(Functional)
50-64%
Credit
(Proficient)
65-74%
Distinction
(Advanced)
75-84%
High Distinction
(Exceptional)
85-100%
Work demonstrates the
knowledge and
understanding of the
best knowledge
representation methods
for the case study
considered in the
assessment
40%
Little or no knowledge of
the best knowledge
representation methods
for the case study
considered in the
assessment. A state space
tree or other standard AI
representation methods
are not used.
Acceptable but further work is
required to show the
knowledge of the best
knowledge representation
methods for the case study
considered in the assessment.
A state space tree or other
standard AI representation
methods are used but include
errors and flaws.
Good level of knowledge
about the best knowledge
representation methods for
the case study considered in
the assessment. A state
space tree or other standard
AI representation methods
are used but not in an
efficient manner for the
problem.
Excellent but not thorough
knowledge about the best
knowledge representation
methods for the case study
considered in the
assessment. A state space
tree or other standard AI
representation methods are
used but it is not robust and
error free for different
mazes.
Excellent and thorough
understanding of the best
knowledge representation
methods for the case study
considered in the
assessment
Work demonstrates the
knowledge and
understanding of the
search algorithm for the
case study considered in
the assessment
40%
Little or no knowledge of
the search algorithms for
the case study considered
in the assessment. The
search method is
attempted but not
implemented correctly.
Acceptable but further work is
required to show the
knowledge of the search
algorithms for the case study
considered in the assessment.
The search method is
implemented but includes
errors and flaws.
Good level of knowledge
about the search algorithms
for the case study
considered in the
assessment. The search
method is implemented but
not in the most efficient
manner.
Excellent but not thorough
knowledge about the search
algorithms for the case
study considered in the
assessment. The search
method is efficient but it is
not robust and error free for
different mazes.
Excellent and thorough
understanding of the search
algorithms for the case
study considered in the
assessment. The search
method is highly efficient,
robust, and error free.
The reflective essay
demonstrates the
knowledge and
understanding of the
whole process of
problem solving using
the best AI problem
solving methods and
practices
The reflective essay
includes no or little
sections and concepts
required. There is no or
little elaborations or
justifications.
The reflective essay includes
some of the sections and
concepts required. There is
little elaborations or
justifications to demonstrate
the knowledge and
understanding of the whole
process of problem solving
using the best AI problem
The reflective essay includes
all the sections and
concepts required.
Elaborations and
justifications are not
discussed well to show the
mastery of the AI technique
used to solve the problem.
The reflective essay includes
all the sections and
concepts required.
Elaborations and
justifications are not
thorough and in-depth to
demonstrate the knowledge
and understanding of the
whole process of problem
The reflective essay includes
all the sections and
concepts required.
Elaborations and
justifications are thorough
and show the mastery of
the process of solving a
simplified real-world
problem using an AI

CAI104 Assessment 2 Brief Project Module 12 Page 5 of 5

20% solving methods and
practices.
solving using the best AI
problem solving methods
and practices.
technique.

 

The following Subject Learning Outcomes are addressed in this assessment
SLO a) Demonstrate an understanding of the field of artificial intelligence including its origins, fundamental concepts, limitations and the problems
that it tries to solve.
SLO b) Explain basic concepts, methods, theories and application for search.
SLO c) Interpret and formulate knowledge representations in the form of logic expressions.
SLO d) Describe knowledge systems, the use of AI based problem solving methods, forms of knowledge representation, and model-based
reasoning.
SLO e) Explain basic concepts, methods, theories and application for learning.
SLO f) Discuss the philosophical foundations and ethics of AI.