School of Natural and Computing Sciences
Department of Computing Science
2024 – 2025
Programming assignment – Groupwork by a team of 4-5 students
Title: JC4004 – Computational Intelligence Note: This assignment accounts for 30% of
the total mark of the course.
Deadline: Submit the assignment in MyAberdeen by 19. December 2024 at 23:00 (China time).
Information for Plagiarism and Collusion: The source code and your report may be submitted for
plagiarism check in MyAberdeen. Please refer to the slides available at MyAberdeen for more
information about avoiding plagiarism before you start working on the assessment. Excessive use
of large language models, such as ChatGPT, for writing the code or the report can also be considered
as plagiarism. In addition, submitting similar work with another group can be considered as
collusion.
Information about Extensions: According to the new extension policy of University of Aberdeen,
teachers are no longer allowed to give deadline extensions for coursework assignments. Extensions
may be requested from the school administration by e-mail: uoa-ji-enquiries@abdn.ac.uk.
Extensions require strong justifications (such as serious illness or grievance), and extension requests
should be accompanied with supporting evidence, such as a medical certificate. See also a separate
document for the extension policy. Since this assignment is a groupwork assignment, extensions
would be granted in very exceptional situations only.
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Introduction
In this assignment, your task is to build an artificial intelligence game bot for playing the traditional
board game Fox and Goose. Your game bot should be able to play the game on both sides, as a fox
and as a goose. The detailed rules of the game are explained below. Please note that there are
different versions of the game: for this assignment, you should follow the rules described in this
document.
Fox and Goose is a two-player board game. One of the players is a fox trying to capture all the geese.
Another player represents the geese and tries to surround the fox so that it cannot move any more.
The game is played on a board with 33 possible locations for the fox and the geese. In the beginning,
there are 15 geese and one fox on the board, as illustrated in the Figure 1. The white pieces are the
geese, and the red piece is the fox.
The game is played in turns. In this version of the game, the fox and the geese can both move one
step horizontally, vertically, or diagonally on their turn along the lines on the board. The player
playing goose can select any of the geese on the board to move. Please note that diagonal movement
is only allowed from some of the positions, as indicated by the lines on the board.
You cannot move a piece to a position that is already taken by another piece. However, the fox can
capture (or eat) a goose by jumping over it to a free position. The captured goose is removed from
the board. It is also possible to capture multiple geese in one turn by chaining the jumps like in
Checkers. A goose cannot capture the fox. It is not mandatory to capture even if it is possible, but it
is mandatory for both the fox and the geese to make a move in their turn. Examples of legal moves
are shown in Figure 2 below.
The goal of the geese is to surround the fox so that it cannot make any legal moves anymore. The
goal of the fox is to capture all the geese. Theoretically, the minimum of four geese would be enough
to surround the fox; therefore, the fox wins when there are less than four geese left on the board.
Examples of winning the game are shown in Figure 3.
Since the fox and the geese have a different goal and follow different rules, the game is unbalanced.
Therefore, the players usually play an even number of games, swapping their roles. The player that
Figure 1. Initial positions in Fox and Goose.
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wins more games is the final winner. In this assignment, your task is to implement the game logic for
both the fox and the goose.
General Guidance and Requirements
In this assignment, you are required to write a Python class Player that is able to play Fox and
Goose game through methods play_fox() and play_goose(). The current game board is
passed to the methods as a parameter, and the methods will return the next move as a fox or as a
goose, respectively. Python file TestFoxAndGoose.py will be shared to demonstrate how the game
testing framework uses the Player class.
Figure 2. Examples of legal moves for the geese (left) and the fox (right), respectively.
Figure 3. Examples of the fox winning the game (left) and the geese winning the game (right).
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The board is a 2-D list object with 7 × 7 characters representing the state of the game. Characters
'F' and 'G' mark the fox and the geese, respectively. An empty position is marked with a dot '.'
and a space ' ' marks a position that is off the playing area. The board is initialised in class
FoxAndGoose in file TestFoxAndGoose.py as follows:
The play_fox() and play_goose()methods in your code should take the board as defined
above as an input parameter. As an output parameter, the method should return a list object with
two or more pairs of integers, where the first value represents the row, and the second value
represents the column on the board. The first pair is the initial position, and the second pair is the
target position. For example, return value [[3,2],[3,3]] means that the piece in the 4th row, 3rd
column will be moved to the 4th row, 4th column. Note that the numbering starts from zero: for
example, position [0,1] is the 2
nd column of the 1
st row.
The play_fox()method can return a longer list with several target positions in case the fox
captures more than just one goose in one move. For example, return value [[3,3],[3,1],
[5,3]] means that the fox first jumps from position [3,3] to position [3,1], capturing the goose
in position [3,2], and then continues to position [5,3], capturing the goose in position [4,2].
You can decide freely what kind of techniques of computational intelligence you use to implement
the game logic. You can implement additional functions and classes if necessary. However, the
Player class should interact with the game framework only through the play_fox() and
play_goose()methods, as described above. The bot should have a reasonable complexity: in the
testing phase, a time limit of 5 seconds will be applied to consider the moves. If your implementation
requires time-consuming initialisation, such as downloading a deep neural network, initialisation
should be done in the class Player constructor __init__, not the play_fox() and
play_goose()methods.
You can use code generation tools and code from external sources moderately for assisting
implementation of parts of the code, but the use of any sources or tools should be explained, and
the references should be given in the project report.
Submission Requirements
You should submit the work in the course page in MyAberdeen. Your submission should include at
least two files: file TeamXX.py that includes the Python code implementing class Player with
methods play_fox() and play_goose(), and ReportXX.pdf that is the project report. In the
file names, replace XX with team number, for example 05. As an example, we provide file Team00.py
that allows you to play the game manually with moves entered by a human user. If your code requires
any additional files to run, such as pre-trained neural network, you should include them also in your
submission.
Please note that it is your responsibility to make sure that the code in TeamXX.py works when we
test it: you should use file TestFoxAndGoose.py to import your class and to test that your code works
with the testing framework. Replace module name Team00 in module=__import__
("Team00") with your own file name without .py extension. If your code has external
dependencies requiring additional installations, they should be clearly explained in the project report
or readme file included in the submission.
Note that the game bots implemented by different groups will play against each other, so it is
essential to ensure compatibility. You should use Python 3. If you use any third-party packages such
as TensorFlow or PyTorch, we recommend using the latest stable version and to avoid using features
with known backwards compatibility problems. We suggest starting with a clean environment and to
keep track of all the installed packages and their version numbers and reporting them in the project
report or readme file.
Note that at the time of writing, the latest TensorFlow version is not compatible with the latest stable
Python release 3.13.0. Therefore, if you plan to use TensorFlow, the Python version should be 3.12.7
or earlier.
The length of the project report should be approximately 1,500 words. It is recommended to include
graphical illustrations, but screenshots of the program code should be avoided. If the code
implements some complex algorithms that are difficult to explain otherwise, flowcharts or
pseudocode can be used as tools of illustration. The report should include the following sections:
1. Introduction: about 200 words.
2. Theoretical basis, including description of the used methods and algorithms with a brief
justification why those techniques were chosen: about 600 words.
3. Implementation details, including the used libraries and e.g., an UML diagram or a list of the
essential methods and their parameters: about 300 words.
4. Conclusions, including self-reflection, difficulties faced, experiences from testing the code,
and ideas for future improvements: about 300 words.
5. Summary of the individual roles, including brief description of team members’ contributions:
about 100 words.
6. References.
If you wish the results for your group to be published in the leaderboard in MyAberdeen, please give
a name for your group in the report! page 6 of 6
Marking Criteria
The assignment will be marked based on the project report (40 marks), methodology (40 marks), and
performance (20 marks).
The project report will be marked according to the coverage of the required aspects, clarity of
presentation (including language and illustrations), consistency between the report and the
submitted code, and relevance of the references.
The methodology will be evaluated based on the suitability of the chosen methods and algorithms
for the given task, creativity (for example, combining different methods in an unconventional way),
and implementation (e.g., clarity of the source code, computational efficiency).
For performance evaluation, we will test all the submitted assignments by arranging them to play
against each other. Every submission will play against each of the other submissions twice, once as a
fox and once as a goose. The results will be aggregated in a league table, where a win gives one point,
and a loss gives zero points. The winner will be awarded 20 marks, and the other groups will be
awarded marks based on the formula:
is the mark for group
is the total points for group , and w**8;𝑖𝑛𝑛Ү**;w**3; is the total points for
the winner of the league.
Note that if both game bots repeat moves back and forth to the same position, the game may end in
a deadlock situation. To resolve deadlocks, the maximum number of moves is set to 1000. If the game
ends without a winner due to a deadlock, both players will be awarded zero points.
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