# COMP1100 Assignment 3 – Othello
In this assignment, you will develop an AI that plays [Othello (also
known as Reversi)](https://en.wikipedia.org/wiki/Reversi), a classic
board game. We have implemented the rules of the game for you, but you
will have to decide how best to play the game.
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This assignment is worth 15% of your final grade.
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**Deadline**: Sunday 27th October, 2019, at 11:00pm Canberra time *sharp*
## Overview of Tasks
This assignment is marked out of 100:
| **Task** | **Marks** |
|———————–|———–|
| Main Task: Othello AI | 55 Marks |
| Unit Tests | 10 Marks |
| Style | 10 Marks |
| Technical Report | 25 Marks |
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As with assignment 2, code that does not compile will be penalised
heavily. It is **essential** that you can write code that compiles and
runs. If you have a partial solution that you cannot get working, you
should comment it out and write an additional comment directing your
tutor’s attention to it.
## Getting Started
Fork the assignment repository and create a project for it in IntelliJ
IDEA, following the same steps as in [Lab
2](https://cs.anu.edu.au/courses/comp1100/labs/02/#forking-a-project). The
assignment repository is at
## Overview of the Game
Othello (also known as Reversi) is a board game played on an 8×8 board
with 64 discs. Each disc is light on one side and dark on the other.
The game starts with 4 discs in the centre of the board: two dark and
two light.
To make a move, a player places a disc with their colour facing up on
an unoccupied square on the board, such that it will capture at least
one of the opponents discs.
Capturing occurs if the placed disc forms a line (horizontal, vertical
or diagonal) that has one or more of the opponents discs on it and is
ended by another of the current player’s discs, with none of the current
player’s discs in-between. All of the opponents
discs on this line are flipped over, becoming the current player’s discs.
It is possible (and in fact quite common) for a single move to capture more
than one line, and all the pieces which can be captured in a move must be
captured (flipped).
The dark player plays first, and play alternates between the two
players. If a player can’t make a legal move, their turn is
skipped. If neither player can make a legal move, the game is over and
the player with the most discs wins.
## Overview of the Repository
Most of your code will be written in `src/AI.hs`, but you will also
need to write tests in `test/OthelloTests.hs`. A small program to play
the game (either Human vs. Human, or Human vs. AI, or AI vs. AI) is
provided at `app/Main.hs`, and you can run it with `cabal run`.
`src/GameState.hs` implements the data structures used to implement
the game rules. The game rules themselves are implemented at
`src/Game.hs`. You should read over both these files. While you may
not need to understand every detail of how they work, understanding
_what_ they do will be helpful.
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The only files you are allowed to modify in your submission are
`src/AI.hs` and `test/OthelloTest.hs`. Any other modifications risk
changing the rules of the game, so you would be playing a different
game to the one in the assignment.
{:.msg-warn} For this assignment, GHC is set to be more pedantic than
the previous two assignments. It will treat all warnings as errors,
and you’ll only be able to write “Safe” Haskell. If you stick with the
tools we’ve taught in the course, you shouldn’t notice this at all,
with one exception: attempts to use `trace` and similar from
`Debug.Trace` will fail to compile. We have chosen to use Safe Haskell
to prevent your AIs from doing underhanded things in the
tournament.
### Other Files
* `app/Main.hs` implements an interface to play the game, and for you
to test your skills against your AI. You are not required to
understand it.
* `comp1100-assignment3.cabal` tells the cabal build tool how to build
your assignment. You are not required to understand this file, and
we will discuss how to use cabal below.
* `src/Config.hs` defines a data type used to configure the
program. You do not need to understand or modify this file.
* The files under `src/Dragons/` contain advanced code that you are
not required to understand or modify. (On medieval maps they drew
pictures of dragons or sea monsters over uncharted areas. The code
in those files is beyond the areas of Haskell which this course
explores.)
* `test/Testing.hs` is a testing library that is similar to the ones
used in previous assignments. It has been extended to allow you to
group related tests together.
* `Setup.hs` tells cabal that this is a normal package with no unusual
build steps. Some complex packages (that we will not see in this
course) need to put more complex code here. You are not required to
understand it.
## Overview of Cabal
As before, we are using the `cabal` tool to build the assignment
code. The commands provided are very similar to last time:
* `cabal build`: Compile your assignment.
* `cabal run othello`: Build your assignment (if necessary), and run
on the test program. We discuss the test program in detail below,
as there are a number of ways to launch it.
* `cabal repl comp1100-assignment3`: Run the GHCi interpreter over
your project.
* `cabal test`: Build and run the tests. This assignment is set up to
run a unit test suite, and as with Assignment 2 you will be writing
tests. The unit tests will abort on the first failure, or the first
call to a function that is `undefined`.
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You should execute these cabal commands in the **top-level directory** of your
project: `~/comp1100/assignment3` (i.e., the directory you are in when you
launch the IntelliJ Terminal tool for your project).
## Overview of the Test Program
When you run `cabal run othello`, you play the game against your AI
called “default”. This is done through a web browser, in the same way
as previous CodeWorld assignments.
You can change the behavior of the program by calling it with
different arguments, like this: `cabal run othello — arg1 arg2
arg3…`. These are the arguments you can provide:
| **Argument** | **Effect** |
|—————–|————————————————————————-|
| `-T GAMETYPE` | Choose how to run the game: `console`/`gui`. |
| `-t TIMEOUT` | How much time to give the AI to make a move. |
| `-p PLAYER1` | Choose the AI name for the dark player, or `HUMAN` to play as a human. |
| `-P PLAYER2` | Choose the AI name for the light player, or `HUMAN` to play as a human. |
| `-H HOSTNAME` | Name of the computer to connect to for a network game. |
| `-n PORTNUMBER` | Port number to host/connect on for a network game. |
| `-h` | Print help text and exit. |
Example: `cabal run othello — -t 0.1 -p default` will play the
default AI against itself, and give it 0.1 seconds to make a move.
The default AI that you get when you clone the repository plays the
first legal move it can find, so playing it against itself should
always look like this:
## Main Task: Othello AI (55 Marks)
### Your Task
Implement an AI (of type `AI`) for Othello in `src/AI.hs`. There is a
list called `ais` in that file, and we will mark the AI you call
“default” in that list.
We will test your AI’s performance by comparing it to implementations
written by course staff, using a variety of standard approaches. Its
performance against these AIs will form a large part of the marks for
this Task.
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It is **vital** that you indicate one AI as “default”, otherwise we will
not know which one to mark.
### Understanding the `AI` Type
The `AI` type is an alias for `Game -> Int -> Position`. The `Game`
argument describes the current state of the game, and the `Int`
argument is an indication of how far you might want to look ahead when
searching for a good move.
You do not have to arrange for your AI to be called; any test program
we provide will do so for you. When it is your AI’s turn, we will call
your AI with the current game state and lookahead `1`, then `2`, then
`3`, etc, until four seconds have passed overall. The most recent
result will be taken as the final result.
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While you are testing, you may want to use the `-t` option to set a
shorter timeout, so that you can test changes more quickly.
Very simple AIs that do not look ahead will ignore the `Int` argument.
### Discussion
Your AI should inspect the `Turn` within the `Game` to see whose turn
it is. You may call `error` if the `Turn` is `GameOver` – your AI
should never be called on a finished game. Your AI can then use the
`Player` value and `opponent` function to work out how to evaluate the
board.
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You may also assume that we will only ever call your AI if there is a
legal move it can make. In particular, this means that we will not
deduct marks for assuming that a list of legal moves is non-empty
(e.g., you used the `head` function). Note that gratuitous use of
`head` and `tail` is still poor style.
This is a very open-ended task, and it will probably help if you build
up your solution a little at a time. We suggest some approaches below.
### First Legal Move
The simplest AI you can build is one that makes the first legal move
it can. We have provided this for you, so you can see what a simple
`AI` looks like.
### Interlude: Heuristics
Heuristic functions were discussed in [the lecture on game
trees](https://cs.anu.edu.au/courses/comp1100/lectures/09-1-Game_Trees.pdf). We
expect the quality of your heuristic function – how accurately it
scores game states – to have a large impact on how well your AI
performs.
### Greedy Strategy
“Greedy strategies” are the class of strategies that make moves that
provide the greatest _immediate_ advantage. In the context of this
game, it means always making the move that will give it the greatest
increase in heuristic. Try writing a simple heuristic and a greedy
strategy, and see whether it beats your “first legal move” AI.
### Interlude: Game Trees
To make your AI smarter, it is a good idea for it to look into the
future and consider responses to its moves, its responses to those
responses, and so on. The lecture on game trees may help you here.
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It is possible for one player to make two moves in a row, if the
opponent has no legal move. Therefore, it is not safe to assume that
each layer of the game tree is scored for opposing players.
### Minimax
Greedy strategies can often miss opportunities that need some
planning, and get tricked into silly traps by smarter opponents. The
Minimax Algorithm was discussed in the lecture on game trees and will
likely give better performance than a greedy strategy.
### Pruning
Once you have Minimax working, you may find that your AI exploring a
number of options that cannot possibly influence the result. Cutting
off branches of the search space early is called _pruning_, and one
effective method of pruning is called [Alpha-Beta
Pruning](https://cs.anu.edu.au/courses/comp1100/lectures/09-2-Alpha_Beta.pdf),
which was discussed in lectures. Good pruning may allow your search to
explore deeper within the time limit it has to make its move.
### Other Hints
* There are four main ways your AI can be made smarter:
– Lookahead: If your function runs efficiently, it can see further
into the future before it runs out of time. The more moves into
the future it looks, the more likely it will find good moves that
are not immediately obvious. Example: at 1 level of lookahead, a
move may let you capture a lot of dics, but at deeper lookahead
you might see that it leaves you open to a large counter-capture.
– Heuristic: You will not have time to look all the way to the end
of every possible game. Your heuristic function guesses how good a
`Game` is for each player. If your heuristic is accurate, it will
correctly identify strong and weak states.
– Search Strategy: This determines how your AI decides which
heuristic state to aim for. Greedy strategies look for the best
state they can (according to the heuristic) and move towards that
state. More sophisticated strategies like Minimax consider the
opponent’s moves when planning.
– Pruning: if you can discard parts of the game tree without
considering them in detail, you can process game trees faster and
acheive a deeper lookahead in the allotted running
time. Alpha-beta pruning is one example; there are others.
* Choosing a good heuristic function is very important, as it gives
your AI a way to value its position that is smarter than just
looking at current score. Perhaps you might find that some squares
are more valuable than others, when it comes to winning games, and
so your AI should value them more highly.
* Do not try to do everything at once. This does not work in production
code and often does not work in assignment code either. Get something
working, then take your improved understanding of the problem to the
more complex algorithms.
* As you refine your AIs, test them against each other to see whether
your changes are actually an improvement.
## Unit Tests (10 Marks)
As with Assignment 2, you will be expected to write unit tests to
convince yourself that your code is correct. The testing code has been
extended from last time – `test/Testing.hs` now allows you to group
tests into a tree structure. As before, you run the tests using `cabal
test`.
### Your Task
Add tests to `test/OthelloTest.hs` that test your AI.
#### Hints
* Most of the hints from Assignment 2 apply here. Reread those.
* If a function is giving you an unexpected result, try breaking it
into parts and writing tests for each part. This helps you isolate
the incorrect parts, and gives you smaller functions to fix.
* If your function has subtle details that need to be correct, think
about writing tests to ensure those details do not get missed as you
work on your code.
## Style (10 Marks)
As you write increasingly complex code, it is increasingly important
that the code remains readable. This saves wasted effort understanding
messy code, which makes it easier to think about the problem and your
solution to it.
### Your Task
Ensure that your code is written in good Haskell style.
## Technical Report (25 marks)
You should write a concise [technical
report](../../resources/06-reports). An excellent report will:
demonstrate conceptual understanding of all major functions, and how
they interact when the program as a whole runs; explain your design
process, including your assumptions, and the reasons behind choices
you made; discuss how you tested your program, and in particular why
your tests give you confidence that your code is correct; and be well
formatted without spelling or grammar errors.
The **maximum word count is 1500**. This is a *limit*, not a *quota*;
concise presentation is a virtue.
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Once again: This is not a required word count. They are the **maximum
number of words** that your marker will read. If you can do it in
fewer words without compromising the presentation, please do so.
Your report must be in PDF format, located at the root of your
assignment repository on GitLab and named `Report.pdf`. Otherwise, it
may not be marked.
The report must have a **title page** with the following items:
* Your name
* Your laboratory time and tutor
* Your university ID
An excellent report will:
* demonstrate conceptual understanding of all major
functions, and how they interact when the program as a whole runs;
* explain your design process, including your assumptions, and the reasons
behind choices you made;
* discuss how you tested your program, and in particular why your tests give
you confidence that your code is correct; and
* be well formatted without spelling or grammar errors.
### Content and Structure
Your audience is the tutors and lecturers, who are proficient at programming
and understand most concepts. Therefore you should not, for example, waste
words describing the syntax of Haskell or how recursion works. After reading
your technical report, the reader should thoroughly understand what problem
your program is trying to solve, the reasons behind major design choices in it,
as well as how it was tested. Your report should give a broad overview of your
program, but focus on the specifics of what *you* did and why.
Remember that the tutors have access to the above assignment
specification, and if your report *only* contains details from it then
you will only receive minimal marks. Below is an potential outline for
the structure of your report and some things you might discuss in it.
#### Introduction
If you wish to do so you can write an introduction. In it, give:
* A brief overview of your program:
– how it works; and
– what it is designed to do.
#### Content
Talk about why you structured the program the way you did. Below are some
questions you could answer:
* Program design
– Describe what each relevant function does conceptually. (i.e. how
does it get you closer to solving the problems outlined in this
assignment spec?)
– How do these functions piece together to make the finished
program? Why did you design and implement it this way?
– What major design choices did *you* make regarding the functions
that *you�ve* written, and the overall structure of your program?
– For this assignment specifically, you could also ask yourself:
– How does your AI select a good move?
– What data structures did you choose, and why?
– How did you develop the AI that is your main submission?
* Assumptions
– Describe assumptions you have made
and how this has influenced your design decisions.
* Testing
– How did you test individual functions?
– Be specific about this – the tutors know that you have tested
your program, but they want to know *how*.
– Describe the tests that prove individual functions on their own
behave as expected (e.g. testing a function with different
inputs and doing a calculation by hand to check that the outputs
are correct).
– How did you test the entire program? What tests did you perform to
show that the program behaves as expected in all (even unexpected)
cases?
* Inspiration / external content
– What resources did you use when writing your program (e.g.,
published algorithms)?
– If you have used resources such as a webpage describing an
algorithm, be sure to cite it properly at the end of your report
in a �References� section. References do not count to the maximum
word limit.
#### Reflection
Discuss the reasoning behind your decisions, rather than *what* the
decisions were. You can reflect on not only the decisions you made,
but the process through which you developed the final program:
* Did you encounter any conceptual or technical issues?
– If you solved them, describe the relevant details of what happened
and how you overcame them.
– Sometimes limitations on time or technical skills can limit how
much of the assignment can be completed. If you ran into a problem
that you could not solve, then your report is the perfect place to
describe it. You could include details such as:
– Theories as to what caused the problem;
– Suggestions of things that might have fixed it; and
– Discussion about what you did try, and the results of these attempts.
* What would you have done differently if you were to do it again?
– What changes to the design and structure you would make if you
wrote the program again from scratch?
* Are parts of the program confusing for the reader? You can explain
them in the report (in this situation you should also make use of
comments in your code).
* If you collaborated with others, what was the nature of the
collaboration? (Note that you are only allowed to collaborate by
sharing ideas, not code.)
– Collaborating is any discussion or work done together on planning
or writing your assignment.
* Other info
– You may like to briefly discuss details of events which were
relevant to your process of design – strange or interesting things
that you noticed and fixed along the way.
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This is a list of **suggestions**, not requirements. You should only
discuss items from this list if you have something interesting to
write.
### Things to avoid in a technical report
* Line by line explanations of large portions of code. (If you want to
include a specific line of code, be sure to format as described in
the “Format” section below.)
* Pictures of code or IntelliJ.
* Content that is not your own, unless cited.
* Grammatical errors or misspellings. Proof-read it before submission.
* Informal language – a technical report is a professional document, and as
such should avoid things such as:
– Unnecessary abbreviations (atm, btw, ps, and so on), emojis, and
emoticons; and
– Stories / recounts of events not relevant to the development of the program.
* Irrelevant diagrams, graphs, and charts. Unnecessary elements will
distract from the important content. Keep it succinct and focused.
If you need additional help with report writing, the [academic skills
writing
centre](http://www.anu.edu.au/students/academic-skills/appointments/academic-skills-writing-centre)
has a peer writing service and writing coaches.
### Format
You are not required to follow any specific style guide (such as APA
or Harvard). However, here are some tips which will make your report
more pleasant to read, and make more sense to someone with a computer
science background.
* Colours should be kept minimal. If you need to use colour, make sure it is
absolutely necessary.
* If you are using graphics, make sure they are *vector* graphics (that stay
sharp even as the reader zooms in on them).
* Any code, including type/function/module names or file names, that
appears in your document should have a monospaced font (such as
Consolas, Courier New, Lucida Console, or Monaco)
* Other text should be set in serif fonts (popular choices are Times,
Palatino, Sabon, Minion, or Caslon).
* When available, automatic *ligatures* should be activated.
* Do not use underscore to highlight your text.
* Text should be at least 1.5 spaced.
## Communication
**Do not** post your code publicly, either on Piazza or via other
forums. Posts on Piazza trigger emails to all students, so if by
mistake you post your code publicly, others will have access to your
code and you may be held responsible for plagiarism.
Once again, and we cannot stress this enough: **do not post your code
publicly** . If you need help with your code, post it *privately* to the
instructors.
When brainstorming with your friends, **do not share code**. There
might be pressure from your friends, but this is for both your and
their benefit. Anything that smells of plagiarism will be investigated
and there may be serious consequences.
Sharing ideas and sketches is perfectly fine, but sharing should stop
at ideas.
Course staff will not look at assignment code unless it is posted
**privately** in piazza.
Course staff will typically give assistance by asking questions,
directing you to relevant exercises from the labs, or definitions and
examples from the lectures.
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Before the assignment is due, course staff will not give individual
tips on writing functions for the assignment or how your code can be
improved. We will help you get unstuck by asking questions and
pointing you to relevant lecture and lab material. You will receive
feedback on your work when marks are released.
## Submission Advice
Start early, and aim to finish the assignment several days before the
due date. At least 24 hours before the deadline, you should:
* Re-read the specification one final time, and make sure you’ve
covered everything.
* Confirm that the latest version of your code has been pushed to
GitLab.
* Ensure your program compiles and runs, including the `cabal test`
test suite.
* Ensure your submission works on the lab machines. If it does not, it
may fail tests used by the instructors.
* Proof-read and spell-check your report.
* Verify that your report is in PDF format, in the root of the project
directory (not in `src`), and named `Report.pdf`. That capital `R`
is important – Linux uses a case-sensitive file system. Check that
you have succesfully added it in GitLab.