haskell代写 comp1100 Assignment 3: Othello Bot

Before you begin

Before you begin this assignment, you should be comfortable writing recursive functions and manipulating lists. If you aren’t, you should revisit the lectures and labs on these topics before attempting this assignment. If you attempt to do this assignment without being able to do these things, you’ll just end up wasting your time and becoming frustrated.

For this assignment, GHC is set to be a bit more pedantic than the previous two assignments. It will treat all warning as errors, and you’ll only be able to write “Safe” Haskell. Assuming that you’re writing good code, you shouldn’t notice this at all, apart from that if you try to use trace and similar from Debug.Trace your code will fail to compile.

Setting things up

1. Go to the assignment’s gitlab repository:https://gitlab.cecs.anu.edu.au/comp1100/comp1100-assignment3. You will need to log in if you are not already.
2. Click on the Fork button to create your own private version of the assignment, so you can work on it independently from everyone else.
3. Click on your name and wait a little while.
4. Your web page should automatically refresh, and if everything went well, display your repository containing the assignment files. You can check this by looking above the banner “The project was successfully forked.”, it should display your name instead of comp1100.
5. Click on “Overview” in the menu on the left.
6. Next to the “Fork” button in your repository, there is a button which says “SSH”. Click on it and switch it to “HTTPS”.
7. Copy this URL.
8. Open IntelliJ. If some other project opens up then close the window: click on the menu File at the top of the IntelliJ window and select Close Project.
9. In the IntelliJ IDEA window, click on Check out from Version Control and select Git.
10. Paste the URL that you copied above into the Clone Repository dialogue box as the Git Repository URL. Set Parent directory where you want your working directory to be created (say ~/comp1100), and leave the directory name as comp1100-assignment3.
11. Click Clone and follow the same IntelliJ steps as Lab 2 starting from the clone step. Make sure you add the upstream remote:

    git remote add upstream https://gitlab.cecs.anu.edu.au/comp1100/comp1100-assignment3.git

Using multiple machines

You may want to work on your own personal computer for this assignment, as well as working in the lab. You can set up an IntelliJ project, cloning your project from GitLab to your personal computer in much the same way, by following the steps starting from the step “Copy this URL” set in bold typeface above.

However, when you switch from computer A (say the labs) to computer B (say your personal computer) you should make sure to commit and push all of your changes back to GitLab from computer A, so you can pull them to computer B before resuming work. Every work session should thus proceed as follows:

1. git pull
2. Do some work…
3. git commit -a -m "<descriptive message goes here>"
4. git push

When you switch to a different machine, use that same sequence. In short, always pull before you start, and always commit and push when you finish.

The task

The task for this assignment is to write an AI (also known in the context of game playing as a bot) which can play the game Othello.

Rules

Othello (also known as Reversi) is board game played on an 8×8 board with 64 discs which are 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 pieces which can be captured in a move must be captured.

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.

Implementation

You don’t need to implement the rules of Othello yourself, we’ve done that for you. The code has been written with readability in mind and is hopefully understandable. If you come across a function you’re unfamiliar with, ask GHCi for its type, search for it on Hoogle, read the documentation and try using it yourself inside GHCi.

The framework

The framework consists of several files. Anything in the file Main.hs can be ignored, as well as anything in the folder Dragons. (On medieval maps they drew pictures of dragons or sea monsters on uncharted areas, and the code contained in these files is beyond the areas of Haskell which this course explores).

That leaves us with 4 files:

• AI.hs
• Config.hs
• Game.hs
• GameState.hs

Of these files, Config.hs may also be safely ignored, but unlike the other files you can ignore, it doesn’t contain anything beyond the scope of this course.

The first file you’ll need to look at is GameState.hs. It contains the data structures for the game (but none of the logic for the game).

The second file you’ll need to familiarise yourself with is Game.hs. It contains the game logic for Othello. It includes functions for checking if a move is legal or not, getting the score for a given Player or actually playing a move.

The final file you need to care about (and arguably the most important file) is AI.hs. Currently it’s fairly empty, containing only three things. A list of AIs with only a single entry, and a single unimplemented AI which is split into two functions, makeBestMove and makeAMove. To start off with, you can ignore makeBestMove and instead focus on makeAMove. This is the only file you may modify (with one exception, mentioned later) and you cannot change the definition of the type AI.

Where to start

First of all, make sure that the initial framework will compile and run on your computer. Start a game by running cabal run othello.

It should say something like “See the game at http://127.0.0.1:3000”. Open this in a web browser, and click on a square to make a move as the Dark player. Unfortunately, the game is going to stop running after that. The default behaviour is to play a game against a bot named "helloWorld", but currently no such bot exists. We need to fix that.

Open up AI.hs in the src folder. Add ("helloWorld", makeFirstLegalMove) to the list of ais. It should now look like.

-- | A list of known AIs and their names.
ais :: [(String, AI)]
ais = [("default", makeBestMove), ("helloWorld", makeFirstLegalMove)]

Alright, we’ve now registered an AI called "helloWorld" which is defined by the function makeFirstLegalMove. Now we just need to define the makeFirstLegalMove function. For Othello we have an 8×8 board, and because we index from 0, a move is defined as pair (col,row) where both col and row are between 0 and 7 (inclusive). We can easily make a list of all possible moves using a list comprehension.

-- | A list of possible moves for the players.
possibleMoves :: [(Int,Int)]
possibleMoves = [(col,row) | col <- [0..7], row <- [0..7]]

Add this to your AI.hs file.

Now, from all these possible moves, we want to return only those which are legal for a given game. By using the functions defined in Game.hs we can write a new function

-- | A list of valid moves from all possible moves for a given game.
legalMoves :: Game -> [(Int,Int)]
legalMoves (Game Nothing _) = [] -- The game is over
legalMoves (Game (Just player) board) =
  filter legalForThisGame possibleMoves
  where
    legalForThisGame (col,row) = legalMove col row player board

So now we have a function, which given a game can tell us what all the legal moves are. We’re now ready to define makeFirstLegalMove.

-- | Pick the first legal move for the game.
makeFirstLegalMove :: AI
makeFirstLegalMove _ game = case legalMoves game of
  [] -> error "makeFirstLegalMove: No Legal moves"
  move:_ -> [move]

Under normal circumstances, an AI would return a list of increasingly better moves until time runs out. However no matter how long we look at it, the first legal move isn’t going to change, so we just return a single move – the first one in our list of legal moves.

Add that to your AI.hs file, save it, and try running the game again (cabal run othello). Hopefully you should now be able to play a full game. If you want to play again just refresh the page.

Now is a good time to commit and push your code.

git commit -a -m"first legal move"
git push

Interlude

Now that you’ve written your first bot, it’s time to write an even better bot. But before you do that, you need to know how to play against it. cabal run othello runs the program with the default settings. That is, a human player playing against an AI called "helloWorld". To do something different than the default settings, you’ll need to give the program arguments. Run cabal run othello -- --help and you should see something like

Usage: othello [OPTION...]
  -T GAMETYPE    --type=GAMETYPE      The type of the game. One of console, gui, host, join and tournament. Defaults to gui.
  -t TIMEOUT     --timeout=TIMEOUT    The timeout (in seconds) for the game.
  -p PLAYER1     --player1=PLAYER1    Player 1 (or the only player for single player games. HUMAN if a human player. Defaults to human.)
  -P PLAYER2     --player2=PLAYER2    Player 2 (HUMAN if a human player). Defaults to the AI helloWorld.
  -h HOSTNAME    --hostname=HOSTNAME  Hostname of the computer to connect to for a network game or tournament.
  -n PORTNUMBER  --port=PORTNUMBER    The port number to connect to or host on for a network game. Defaults to 9001.
  -h             --help               Prints this help message and exits.

As you can see, there are more modes of playing the game than just the GUI in the web browser, but the two most important options are --player1 and --player2. So let’s say you wanted to play with two human players. You’d start the game with cabal run othello -- --player1=HUMAN --player2=HUMAN. Of course, since the default for player 1 is already human, you can omit that, and if you don’t feel like typing so much, you can use the short option for player 2, giving you cabal run othello -- -P HUMAN. Or if for example you wanted to watch two of the "helloWorld" AIs face off, you can run cabal run othello -- --player1=helloWorld --player2=helloWorld.

The one exception to the “only modify AI.hs rule” is that you may modify Config.hs so that player 2 has a different default AI than "helloWorld" (for example to the aptly named "default" AI).

Check that you’ve understood this by playing your "helloWorld" bot against itself. The final position should look like.

Making a good move

To make a good AI, we need to have a way of deciding what is, and what isn’t a good move. A good place to start, is by trying to work out what are, and what aren’t, good board positions. As you may have noticed, the score given in the game can move by quite a bit in a single turn, so by itself it may not be the best indicator of whether or not a board position is good.

The first step on the way to success is writing what’s known as a greedy bot. This is simply an AI, who looks at all possible next moves, and picks the move which will give it the best board position. Now if we had a perfect function for rating board positions, we could stop here. Unfortunately Othello is not a solved game (yet).

If you have a greedy bot working, now would be a good time to commit and push.

git commit -a -m"greedy bot"
git push

Making a better move

Unless your scoring function is highly unusual, the closer to the end of the game you get, the more accurate it will be. If we’re lucky it can also identify the difference between a position that’s bad for us, and a position that’s really bad for us.

So instead of looking just one turn into the future like a greedy bot would, we can try looking several turns into the future, both to increase the accuracy of our scoring function, and avoiding the common pitfall that a greedy bot has of playing a move that seems really good, only for the opponent to have an even better move next turn.

Minimax

There’s a commonly used algorithm in games like Othello for doing just this – it’s known as minimax. It operates under the assumption that both you and your opponent are playing the best moves at each step.

Here’s a rough outline of how it works.

1. Pick a depth that you’re going to lookahead to.
2. Build out a game tree to the given depth for each possible move.
3. Give each node a score, for a leaf node it’s the score that your scoring function gives the board, and for an inner node its value is either the maximum (if it’s our turn in the game at the current node) or the minimum (if it’s our opponent’s turn in the game at the current node) of the value of its children.
4. Pick the move which has the game tree who’s root node has the highest score.

When you write an AI, you’ll probably call this function several times in a single turn with increasingly greater depths until it runs out of time. Unfortunately in the GUI, the depth of the search isn’t displayed, but if you run it in console mode (remember, you can see the options at any time with cabal run othello -- --help) it will tell you how far it looks ahead. If it’s returning a very large number, that probably means you’ve searched to the end of the tree.

There are several optimisations to minimax, but one of the most effective is known as Alpha Beta Pruning.

Writing a stronger AI

Once you’ve gotten a bot with Minimax with Alpha-Beta pruning, there’s still room for improvement.

• Try and sort your moves for Alpha Beta pruning to have a better move ordering, since Alpha Beta pruning works best if looks at the best moves first.
• There are algorithms such as Negascout or MTD(f) which attempt to improve on Alpha-Beta pruning.
• If most of your time is spent calculating new boards, because the provided code is slow, you can write your own faster version of Othello (although if you do, all code must go in AI.hs).
• You can go back and tweak your heuristic.

Deliverables

Code (70%)

You’re required to submit an AI which can play Othello called "default”. (i.e if I callcabal run othello -- -p "default" -P "default" I should be able to watch it play against itself.)

You can include as many other AIs as you’d like, but "default" should be your strongest. It doesn’t need to implement any algorithm in particular, but it should be more advanced than a greedy bot.

The only file which will be looked at is your AI.hs file. It must be able to work without modification with the initial assignment framework. If it doesn’t this will be treated the same as having non-compiling code.

Report (30%)

You are required to write a concise technical report explaining your design choices in completing your program, its implementation, a self-critique of your work, and potential improvements you could make. It must be no longer than 1500 words.

Your report must be in PDF format, located at the root of your assignment repository (the folder with othello.cabal, not in the src folder) 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’s name.
• Your university ID
• Collaborators (if any)

Content

Keep in mind that your audience is your tutors and the lecturers, who are proficient at programming and understand most programming concepts. Therefore, for example, describing how minimax¹works in your report is not necessary.

After reading a technical report, the reader should be comprehensively aware of what problem the program is trying to solve, the reasons for major design choices in the program, as well as how it was tested.

Below is a list of questions that you might consider discussing in your report. These are only suggestions, you don’t need to answer all of them.

• How does your AI select a move?
• What data structures did you use?
• Did you write several different AIs or just keep improving one?
• What were the conceptual or technical issues that you encountered whilst doing the assignment, and how did you get past them?
• What were the assumptions that you had to make during the assignment?
• What would you have done differently if you were to do it again?
• How would you make your AI better?
• How did you test your code?
• Which parts of your code might be confusing for the reader? Explain them if so.

Aside from what should be in your report, here are some things that should definitely not be in it:

• Any content that is not your own.
• Grammatical errors or misspellings. Proof-read it before submission.
• Informal language. This includes unnecessary abbreviations (atm, btw, ps, and so on), emojis, and emoticons. Keep in mind that this is a professional document.
• Diagrams, graphs, and charts that are not relevant. Any unnecessary elements will distract from the actual content. Keep it succinct and focused.

Communicating

Do not post your code publicly, either on Piazza or via other forums. If by mistake you post your code publicly on Piazza, an email containing your post with your code will be sent to all students. This means that 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 show your code to them. There might be some pressure from your friends and even some judgements that you are not sharing the code, but it is for both your and their benefit.

Anything that smells of plagiarism will be dealt with seriously and there may be serious consequences.

Sharing ideas is perfectly fine, but sharing should stop at ideas. In particular for this assignment you may wish to discuss board evaluation heuristics. You may do so, but under no circumstances give anyone else your code for scoring a board.

Course staff will not look at assignment code unless it is posted privately in piazza.

Course staff will typically give assistance by directing you to relevant exercises from the labs, or definitions and examples from the lectures.

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. You will receive their feedback when the mark is released.

Submission Checklist

Preferably 24 hours prior to the assignment deadline, you should make sure that:

• You’ve fully read and understand the assignment specification.
• Your code works on the lab machines (it both compiles and the AI "default" can play a game without crashing.)
• Your latest commit is pushed to GitLab. Ensure this by going to the web interface of GitLab and check manually.
• Your report is on GitLab, as a PDF

Some of the marks will be attributed to the styling of your code. Thus it’s important that you do not have any obfuscated code or unnecessary repetitions in the code. Your file should be correctly indented uniformly throughout and lines should not be longer than 80 characters long.

Marking

The marking is divided roughly into 70% for the code and 30% for the report. For exceptional reports or exceptional code we might take the liberty to shift those percentages slightly to acknowledge extra efforts.

Marks in the code will be awarded for functionality, clarity, and how effectively it can play Othello.

Marks in the report will be awarded to concise writing, completeness of the report, and your understanding of your own designs and findings.

Here is a breakdown of example student cases with different marks awarded:

Grade	Criteria
Pass	Code compiles and the AI can reliably beat an AI who makes the first legal move. The report shows basic understanding of the problem and explains the submitted code.
Credit	Code compiles and the AI can reliably beat a greedy AI with a weak heuristic. The code is well structured and well supported in the report.
Distinction	Code compiles and the AI can reliably beat a minimax AI with a weak heuristic. The code is well structured and the report is excellently written.
High Distinction	Code compiles and the AI can reliably beat a minimax AI with an above average heuristic and pruning. The report is excellently written and explains in detail which additional measures have been taken and how they have been implemented.

Roughly, you can expect to reach a distinction level mark with an excellent submission of a minimax based solution with a strong heuristic, or a minimax solution with pruning and a weak heuristic.

Final Words

There may be a tournament involving all of your submissions, but if there is, it will be anonymous and its results will have no impact on your final mark.

There may also be some additions or changes made to the framework, so pull from upstream regularly. However, barring any large issues being found, AI.hs, Game.hs and GameState.hs will not change at all.