Project Part B
Playing the Game
COMP30024 Artificial Intelligence
13 April 2022
1 Overview
In this second part of the project, we will play the full two-player version of Cachex. Before you read this
specification you should re-read the ‘Rules for the Game of Cachex ’ document. The rules of the game are
the same as before, however we have further clarified some points of confusion raised by students over the
past couple of weeks.
The aims for Project Part B are for you and your project partner to (1) practice applying the game-
playing techniques discussed in lectures and tutorials, (2) develop your own strategies for playing Cachex,
and (3) conduct your own research into more advanced algorithmic game-playing techniques; all for the
purpose of creating the best Cachex–playing program the world has ever seen.
The task
Your task is twofold. Firstly, you will design and implement a program to play the game of Cachex.
That is, given information about the evolving state of the game, your program will decide on an action
to take on each of its turns (we provide a driver program to coordinate a game of Cachex between two
such programs so that you can focus on implementing the game-playing strategy). Section 2 describes this
programming task in detail, including information about how our driver program will communicate with
your player program and how you can run the driver program.
Secondly, you will write a report discussing the strategies your program uses to play the game, the
algorithms you have implemented, and other techniques you have used in your work, highlighting the most
impressive aspects. Section 3 describes the intended structure of this document.
The rest of this specification covers administrative information about the project. For assessment criteria,
see Section 4. For submission and deadline information see Section 5. Please seek our help if you have any
questions about this project.
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2 The program
You must create a program in the form of a Python 3.6 module named with your team name.
2.1 The Player class
When imported, your module must define a class named Player with at least the following three methods:
1. def init (self, player, n): Called once at the beginning of a game to initialise your player.
Use this opportunity to set up an internal representation of the game state.
The parameter player will be the string "red" if your program will play as Red, or the string "blue"
if your program will play as Blue. The parameter n denotes the size of the board being used.
2. def action(self): Called at the beginning of your turn. Based on the current state of the game,
your program should select and return an action to play.
The action must be represented based on the instructions for representing actions in the next section.
3. def turn(self, player, action): Called at the end of each player’s turn, after the referee has
validated and applied that player’s action to its game state. Use this opportunity to update your own
internal representation of the game state.
The parameter player will be the player whose turn just ended (either "red" or "blue"), and action
will be the action performed by that player. Of course, if it was your turn that just ended, action
will be the same action you returned through the action method. Again, actions will be represented
following the instructions for representing actions in the next section. The action will always be valid
since the referee performs validation before this method is called (e.g., your turn method does not
need to validate the action against the game rules).
2.2 Representing actions
Our programs will need a consistent representa-
tion for actions. We will represent all actions as tu-
ples containing first a string action type and then
possible action arguments, indexing hexes with the
axial coordinate system from Part A (see Figure 1).
To represent a steal action, use a tuple:
("STEAL", )
There are no arguments required. As per the
game rules this action may only be played by
Blue as their first move of the game.
? To represent a place action use a tuple:
("PLACE", r, q)
The arguments r, q denote the coordinate of the
token being placed on the game board. Note
that there is no need for a separate capture
action since captures occur as a consequence
of placing tokens.
Figure 1: The r and q axes, with (r, q) indices for
all hexes (n = 5). For notes on a similar axial coor-
dinate system, see redblobgames.com/grids/hexagons/
(and don’t forget to acknowledge any algorithms or
code you use in your program).
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2.3 Running your program
To play a game of Cachex with your program, we provide a driver program—a Python module called
referee. For your information, the referee program has the following essential structure:
1. Set up a Cachex game. Initialise the Player class for each of Red and Blue, as directed by the command
line arguments (calling their . init () methods). Set the active player to Red, since they always
begin the game as per the rules.
2. Repeat the following until the game ends:
(a) Ask the active player for their next action (calling their .action() method).
(b) Validate the action and apply it to the game if is allowed (otherwise, end the game with an error
message). Display the resulting game state to the user.
(c) Notify both players of the action (calling their .turn() methods).
(d) Switch the active player to facilitate turn-taking.
3. After detecting one of the ending conditions, display the final result of the game to the user.
To play a game using referee, invoke it as follows. The referee module (the directory referee/) and
the modules with your Player class(es) should be within your current directory:
python -m referee
where python is the name of a Python 3.6 interpreter1 , is the size of the game board and
and are the names of modules containing the classes Player to be used for Red and Blue, re-
spectively. The referee offers many additional options. To read about them, run ‘python -m referee --help’.
2.4 Game board size
Cachex may theoretically be played on a hex board of arbitrary size, denoted by the parameter n. However,
for practical purposes we will constrain testable n values to the range [3, 15] (inclusive). In fact, the referee
program will not accept values outside of this range. While your agent must be capable of playing a game
for any n inside this range, you should first prioritise optimising it for n = 8 and n = 9. The majority of
assessed test cases will use these board sizes.
2.5 Program constraints
The following resource limits will be strictly enforced on your program during testing. This is to prevent
your programs from gaining an unfair advantage just by using more memory and/or computation time. These
limits apply to each player for an entire game. In particular, they do not apply to each turn separately. For
help measuring or limiting your program’s resource usage, see the referee’s additional options (--help).
A maximum computation time limit of n2 seconds per player, per game.
A maximum memory usage of 100MB per player, per game (not including imported libraries).
You must not attempt to circumvent these constraints. For example, do not use multiple threads or attempt
to communicate with other programs or the internet to access additional resources.
1 Note that Python 3.6 is not available on dimefox by default. However, it can be used after running the command
‘enable-python3’ (once per login).
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2.6 Allowed libraries
Your program should use only standard Python libraries, plus the optional third-party librariesNumPy
and SciPy (these are the only libraries installed on dimefox). With acknowledgement, you may also include
code from the AIMA textbook’s Python library, where it is compatible with Python 3.6 and the above
limited dependencies. Beyond these, your program should not require any other libraries in order
to play a game.
However, to develop your program, you may use any tools, including tools using other programming
languages. This is all allowed as long as your Player class does not require these tools to be available when
it plays a game (because they are not available on dimefox).
For example, let’s say you want to use machine learning techniques to improve your program. You could
use third-party Python libraries such as scikit-learn/TensorFlow/PyTorch to build and train a model. You
could then export the learned parameters of your model. Finally, you would have to (re)implement the
prediction component of the model yourself, using only Python/NumPy/SciPy. Note that this final step is
typically simpler than implementing the training algorithm, but may still be a significant task.
3 The report
Finally, you must discuss the strategic and algorithmic aspects of your game-playing program and the
techniques you have applied in a separate file called report.pdf.
This report is your opportunity to highlight your application of techniques discussed in class and beyond,
and to demonstrate the most impressive aspects of your project work.
3.1 Report structure
You may choose any high-level structure of your report. Aim to present your work in a logical way, using
sections with clear titles separating different topics of discussion.
Here are some suggestions for topics you might like to include in your report. Note: Not all of these topics
or questions will be applicable to your project, depending on your approach. That’s completely normal. You
should focus on the topics which make sense for you and your work. Also, if you have other topics to discuss
beyond those listed here, feel free to include them.
? Describe your approach: How does your game-playing program select actions throughout the game?
Example questions: What search algorithm have you chosen, and why? Have you made any modi-
fications to an existing algorithm? What are the features of your evaluation function, and what are
their strategic motivations? If you have applied machine learning, how does this fit into your overall
approach? What learning methodology have you followed, and why? (Note that it is not essential to
use machine learning to design a strong player)
Performance evaluation: How effective is your game-playing program?
Example questions: How have you judged your program’s performance? Have you compared multiple
programs based on different approaches, and, if so, how have you selected which is the most effective?
Other aspects: Are there any other important creative or technical aspects of your work?
Examples: algorithmic optimisations, specialised data structures, any other significant efficiency opti-
misations, alternative or enhanced algorithms beyond those discussed in class, or any other significant
ideas you have incorporated from your independent research.
Supporting work: Have you completed any other work to assist you in the process of developing
your game-playing program?
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Examples: developing additional programs or tools to help you understand the game or your program’s
behaviour, or scripts or modifications to the provided driver program to help you more thoroughly
compare different versions of your program or strategy.
You should focus on making your writing succinct and getting the level of detail right. The appropriate
length for your report will depend on the extent of your work and so aiming for succinct writing will be more
appropriate than aiming for a specific word or page count.
For example, there’s probably no need to present detailed code inside your report. Moreover, there’s no
need to re-explain ideas we have discussed in class (and if you have applied a technique or idea that you
think we may not be familiar with, then it would be appropriate to write a brief summary of the idea and
provide a reference through which we can obtain more information).
3.2 Report constraints
While the structure and contents of your report are flexible, your report must satisfy the following constraints:
Your report must not be longer than 6 pages (excluding references, if any).
Your report can be written using any means but must be submitted as a PDF document.
4 Assessment
Your team’s Project Part B submission will be assessed out of 22 marks, and contribute 22% to your final
score for the subject. Of these 22 marks:
11 marks will be allocated to the level of performance of your final player (you can only submit one,
so pick your best if you developed a few players).
Of these, 4 marks are available for a player capable of consistently completing games without syn-
tax/import/runtime errors, without invalid actions, and without violating the time or space constraints.
The remaining marks are awarded based on the results of testing your player against a suite of hidden
‘benchmark’ opponents of increasing difficulty, as described below. In each case, the mark will be based
on the number of games won by your player (multiple test games will be played against each opponent
with your player playing as Red and Blue in equal proportion). All tests will use Python 3.6 on the
student Unix machines (for example, dimefox2).
3 marks available: Opponents who choose randomly from their set of allowed actions each turn.
2 marks available: ‘Greedy’ opponents who select the most immediately promising action available
each turn, without considering your player’s responses (for various definitions of ‘most promising’).
2 marks available: Opponents using the adversarial search techniques discussed in class and a simple
evaluation function to look an increasing number of turns ahead.
11 marks will be allocated to the successful application of techniques demonstrated in your work.
We will review your report (and, on occasion, your code3) to assess your application of adversarial game-
playing techniques, including your game-playing strategy, your choice of adversarial search algorithm,
and your evaluation function. For top marks, we will also assess your level of exploration beyond
techniques discussed in class for enhancing the effectiveness of your player. Note that your report
will be the primary means for us to assess this component of the project, so please use
it as an opportunity to highlight your successful application of techniques. For more detail, see the
following criteria:
2We strongly recommended that you test your program on dimefox before submission. Note that Python 3.6 is not available
on dimefox by default, but it can be used after running the command‘enable-python3’ (once per login).
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0–5 marks: Work that does not demonstrate a successful application of important techniques dis-
cussed in class for playing adversarial games. For example, a player who just makes random
moves would likely get 0 marks.
6–7 marks: Work that demonstrates a successful application of the important techniques discussed
in class for playing adversarial games, possibly with some theoretical, strategic, or algorithmic
enhancements to these techniques.
8–9 marks: Work that demonstrates a successful application of the important techniques discussed
in class for playing adversarial games, along with many theoretical, strategic, or algorithmic
enhancements to these techniques, possibly including some significant enhancements based on
independent research into algorithmic game-playing or original strategic insights into the game.
10–11 marks: Work that demonstrates a highly successful application of important techniques dis-
cussed in class for playing adversarial games, along with many significant theoretical, strategic,
or algorithmic enhancements to those techniques, based on independent research into algorithmic
game-playing or original strategic insights into the game, leading to excellent player performance.
As per this marking scheme, it is possible to secure a satisfactory mark by successfully applying the
techniques discussed in class. Beyond this, the project is open-ended. Every year, we are impressed by what
students come up with. However, a word of guidance: We recommend starting with a simple approach before
attempting more ambitious techniques, in case these techniques don’t work out in the end.
In the rare case where there is a disagreement between students about their contributions to the project
which they cannot resolve within the group, we may assign a different mark to each team member.
4.1 Academic integrity
Unfortunately, we regularly detect and investigate potential academic misconduct and sometimes this leads
to formal disciplinary action from the university. Below are some guidelines on academic integrity for this
project. Please refer to the university’s academic integrity website (academicintegrity.unimelb.edu.au),
or ask the teaching team, if you need further clarification.
1. You are encouraged to discuss ideas with your fellow students, but it is not acceptable to share
code between teams, nor to use code written by anyone else. Do not show your code to
another team or ask to see another team’s code.
2. You are encouraged to use code-sharing/collaboration services, such as GitHub, within your team.
However, you must ensure that your code is never visible to students outside your team.
Set your online repository to ‘private’ mode, so that only your team members can access it.
3. You are encouraged to study additional resources to improve your Python skills. However, any code
adapted or included from an external source must be clearly acknowledged. If you use code
from a website, you should include a link to the source alongside the code.
4. If external or adapted code represents a significant component of your program, you should also ac-
knowledge it in your report. Note that for the purposes of assessing your successful application of
techniques, using substantial amounts of externally sourced code will count for less than an original
implementation. However, it’s still better to properly acknowledge all external code than to submit it
as your own in breach of the university’s policy.
3We will not assess the ‘quality’ of your submitted code. We may seek to clarify and verify claims in your report by referring
to your implementation. For at least this reason, you should submit well-structured, readable, and well-documented code.
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5 Submission
One submission is required from each team. That is, one team member is responsible for submitting all of
the necessary files that make up your team’s solution.
You must submit a single compressed archive file (e.g. a .zip or .tar.gz file) containing all files making
up your solution via the ‘Project Part B Submission’ item in the ‘Assessments’ section of the LMS. This
compressed file should contain all Python files required to run your program (with the correct directory
structure), along with your report. In addition, if you have created any extra files to assist you while
working on this project,4 then all of these files are worth including when you submit your solution.
The submission deadline is 11:00PM on Wednesday the 11th May, Melbourne time (AEST).
You may submit multiple times. We will mark the latest submission made by either member of your
team unless we are advised otherwise. You may submit late. Late submissions will incur a penalty of two
marks per working day (or part thereof) late.
5.1 Extensions
If you require an extension, please email the lecturers using the subject ‘COMP30024 Extension Request’ at
the earliest possible opportunity. If you have a medical reason for your request, you will be asked to provide
a medical certificate. Requests for extensions received after the deadline may be declined.
4For example you may have created alternative player classes, a modified referee, additional programs to test your player
or its strategy, programs to create training data for machine learning, or programs for any other purpose not directly related
to implementing your player class. As long as these files are not too large, you are encouraged to include them with your
submission (and mention them in your report).