Assignment 3: Fanorona
Skip navigation Programming as Problem Solving (including Advanced) ANU School of Computing Search query Search ANU web, staff & maps Search current site content Search Menu Search query Search COMP1100/1130 Lectures Labs 1130 Problems Assignments Exams Resources Search ANU web, staff & maps Search current site content COMP1100/1130 Lectures Labs 1130 Problems Assignments Exams Resources menu Search query Search COMP1100/1130 Search query Assignments Assignment 1: Shapes Assignment 2: Cellular Automata Assignment 3: Fanorona News Final Exam Details Assignment Three has been released Change of Lecture Venues » View all Related sites 2021 Semester 2 page Echo360 (for lecture recordings) Piazza discussion forum Current students Programs and courses Timetabling Exam Scheduling Academic Skills You are here » Assignments » Assignment 3: Fanorona In this assignment, you will develop a software agent or AI bot that plays Fanorona. We have implemented the rules of the game for you. Your task is to decide how is best to play. This assignment is worth 15% of your final grade. Deadline: Sunday 29 May, 2022, at 11:00pm Canberra time sharp Overview of Tasks This assignment is marked out of 100 for COMP1100 students, and 120 for COMP1130. Task COMP1100 COMP1130 Main Task: Fanorana AI 55 Marks 65 Marks Unit Tests 10 Marks 10 Marks Coding Style 10 Marks 10 Marks Technical Report 25 Marks 35 Marks As with assignment 2, code that does not compile will be penalised heavily. This means that both the commands cabal v2-run game (with sensible arguments) and cabal v2-test must run without errors. If either command fails with an error, a significant mark deduction will be applied. 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. You are also welcome to ask questions about error messages in drop-ins or on piazza. Getting Started Fork the assignment repository and clone it to your computer, following the same steps as in Lab 2. The assignment repository is at https://gitlab.cecs.anu.edu.au/comp1100/2022s1/2022s1studentfiles/assignment3. Overview of the Game Fanorona is a two-player strategy boardgame from Madagascar. The version we will (roughly) be playing, Fanorona-Tsivy, is played on a \(9\times 5\) board with the following starting configuration: Tiles are placed on vertices and may move along any edge from a given vertex. Note that on this style of board, sometimes known as an Alquerque board, diagonals are sometimes (but not always) available. The player with light coloured tiles moves first, and moves are split into three main categories: Capturing moves. A player may capture a contiguous line of opponent pieces by either approaching or withdrawing from such a contiguous line. Such a move is classified as either an Approach or a Withdrawal, and defined by the Locations the piece moves from and to. At the start of a turn, a player must make a capturing move if it is possible to do so. After making such a move, lets call the piece that moved the Captor. If it is possible for the Captor to move again, provided it visits no Location more than once in a turn, the player may make another move. If, after a turn has started, the Captor cannot make a capture, then the turn is over and it is the other player’s turn. Pass [COMP1130 Students Only] A player may terminate a turn early by making a Pass. Note that a player cannot Pass unless a turn has already started (i.e., there is a Captor). Note that Pass moves will not appear in the list of legal moves. It is up to you to decide when they should be considered, but they must be considered (and can only be called when legal to do so). COMP1100 students use a slightly simplified version of the game where turns of capturing sequences must be completed, not truncated by passing. Paika If, at the start of a turn there is no possible capturing move, then you may make a Paika move, moving any of your pieces to an adjacent empty vertex. this also concludes the turn. The game is over if either a board position is repeated (this can only occur after a sequence of Paika moves) — in this case ending in a draw — or when one player is not able to make a move (typically because they have used all their pieces). The player who made the last move is then the winner, and the player who cannot move the loser. The game will also end in a draw if there have been more than 20 consecutive Paika moves, but this should be very rare. Overview of the Repository Most of your code will be written in src/AI.hs, but you will also need to write tests in src/AITests.hs. Other Files src/Fanorona.hs implements the rules of Fanorona. You should read through this file and familiarise yourself with the data declarations and the type signatures of the functions in it, as you will use some of these to analyse the game states. You do not need to understand how the functions in this file works in detail. src/FanoronaTests.hs implements some unit tests for the game. You are welcome to read through it. src/AITests.hs is an empty file for you to write tests for your agent. src/Testing.hs is a simple test framework similar to the one in Assignment 2. However, it has been extended so that you can group related tests together for clarity. src/Dragons contains all the other code that makes the framework go. You do not need to read or understand anything in this directory. Here be dragons! (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. 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. comp1100-assignment3.cabal tells the cabal build tool how to build your assignment. We will discuss how to use cabal below. .ghcid tells the ghcid tool which command to run, which is what supplies VSCodium with error highlighting that automatically updates when you save a file. .gitignore tells git which files it should not put into version control. These are often generated files, so it doesn’t make sense to place them under version control. 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 v2-build: Compile your assignment. cabal v2-run game: Build your assignment (if necessary), and run 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 so you can test functions interactively. cabal v2-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. cabal v2-haddock: Generate documentation in HTML format, which you can read with a web browser. This might be a nice way to read a summary of the game module, but it also documents the Dragons modules which you can safely ignore. 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 a terminal from VSCodium). Overview of the Test Program To run the test program, you need to provide it with command line arguments that tell it who is playing. This command will let you play against the current "default" AI bot. Before you replace this with your own bot, the default will be firstLegalMove playing with COMP1100 rules: cabal v2-run game -- --p1 human --p2 ai
using ai to get the default ai is part of how we mark your assignment, so it is vital that you update your default bot to be whatever you want to be marked! To play with a differently named AI, say, "greedy, use: cabal v2-run game -- --p1 human --p2 ai:greedy
In general, the command to run the game looks like this: cabal v2-run game -- ARGS
Replace ARGS with a collection of arguments from the following list: --comp1130: Run the COMP1130 version of the game (with Pass enabled). --timeout DURATION: Change the amount of time (in decimal seconds) that AI functions are given to think of a move (default = 4.0). You may want to set this to a smaller number when testing your program, so that things run faster. --height LENGTH and --width LENGTH - Alter the size of the board to the given value (rounded up to the nearest odd number - Fanorona only makes sense on odd size boards). The default values are 9 and 5 respectively, and your implementations will only be tested on that size. Your AI does not need to work for differently sized boards, but you may want to test it on simpler boards if it does! --debug-lookahead: When an AI is done thinking, print out how many moves ahead it considered, and the candidate move it came up with at each level of lookahead. The first item in the printed list is the move it came up with at lookahead 1, the second item is the move it came up with at lookahead 2, and so on. --ui codeworld: Show the game using CodeWorld. This is the default user interface. Use your web browser to play the game, as in previous assignments. Unlike the codeworld programs in previous assignments, you must terminate the program with Ctrl-C and restart it if you want to restart your game. --ui text: Show the game in the terminal. --ui json: Run a non-interactive game (i.e., AI vs. AI, or AI vs network), and output a report of the game in JSON format. You probably won’t have a use for this, but it’s documented here for completeness. --host PORT: Listen for a network connection on PORT. You only need this for network play (see below). --connect HOST:PORT: Connect to someone else’s game. You only need this for network play (see below). --p1 PLAYER: Specify the white player. Required. --p2 PLAYER: Specify the black player. Required. The PLAYER parameters describe who is playing, and can take one of the following forms: Format Effect human Ask the person at the computer for moves. ai Ask the "default" AI for moves. ai:AINAME Ask a specific AI for moves (example: ai:firstLegalMove). network Wait for a move from the network. Network Play Network play is provided in the hope that it will be useful, but we are unable to provide support for this feature, or diagnose problems related to tunnelling network connections between computers. The assignment framework supports network play, so that you can test agents against each other without sharing code. One machine must host the game, and the other machine must connect to the game. In the example below, machine A hosts a game on port 5000 with the agent crashOverride as player 1, then machine B connects to the game, providing the AI chinook as player 2: # On Machine A:
cabal v2-run game -- --host 5000 --p1 ai:crashOverride --p2 network
# On Machine B (you'll need Machine A's external IP address somehow):
cabal v2-run game -- --connect 198.51.100.66:5000 --p1 network --p2 ai:chinook
Under the bonnet, the network code makes a single TCP connection, and moves are sent over the network in JSON. You will need to set up your modem/router to forward connections to the machine running your assignment. A service like ngrok may help, but as previously mentioned we are unable to provide any support for this feature. Main Task: Fanorona AI (COMP1100: 55 Marks; COMP1130: 65 Marks) Implement an AI (of type AIFunc, defined 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. This list is also where the framework looks when it tries to load an AI by name. 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. It is vital that you indicate one AI as "default", otherwise we will not know which one to mark. TO indicate an AI as "default", you must have a (String, AIFunc) pair in the ais list of AIs in src/AI.hs where the String is precisely "default". Understanding the AIFunc Type The AIFunc type has two constructors, depending on whether you are implementing a simple AI that looks only at the current state, or a more complicated AI that performs look-ahead. The NoLookahead constructor takes as its argument a function of type GameState -> Move. That is, the function you provide should look at the current state of the game and return the move to play. This constructor is intended for very simple AIs that do not look ahead in the game tree. As such, this function should never run for more than a moment at a time, but nevertheless, it is also subject to the timeout of 4 seconds. The WithLookahead constructor takes as its argument a function of type GameState -> Int -> Move. The Int parameter may be used to represent how many steps you should try to look ahead in the game tree. The assignment framework will call your function over and over, with look-ahead 1, then 2, then 3, etc., until it runs out of time. The framework will take the result of the most recent successful function call as your AI’s best move. If your AI does not return a move in time, the program will stop with an error. Getting Started 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. 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 otherPlayer function to work out how to evaluate the board. When you call legalMoves on a given GameState, it will never return a list containing Pass, even when such a move is legal. COMP1130 students are expected to consider passing moves in your implementation, so you must determine when passing moves should be considered explicitly. 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. Note that, in general, you cannot make this assumption about GameStates you have generated within your AI function. 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 are discussed in the lecture on game trees. 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. Bear in mind that in a game like this firstLegalMove will not play terribly, as it still must capture when given the opportunity. Considering Whole Turns In this implementation of Fanorona, making a move does not necessarily mark the end of your turn. In cases where a turn may contain multiple moves, you may want to consider how to select the best move in terms of your turn as a whole. Abstracting multiple moves into a single turn may also help with conceptualising game trees (discussed below), as a game tree of moves may have differing turns at the same depth. In a game tree of turns you can always be sure that the player alternates at every level in the tree. 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. 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 as 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: Look-ahead: 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 look-ahead, a move may let you capture a lot of pieces, but at deeper look-ahead 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 achieve a deeper look-ahead 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 bots, 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—src/Testing.hs now allows you to group tests into a tree structure. As before, you run the tests using cabal v2-test. Your Task Add tests to src/AITests.hs to test your AI. Hints Most of the hints from Assignment 2 apply here. Re-read 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. Coding 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. If you wish, you know how, and you have a good reason, you may split your code into multiple modules. However this is not a requirement, and you will never be penalised for not doing so. You MUST NOT edit any of the files in the framework (with the obvious exceptions of AI.hs and AITests.hs. You may also edit FanoronaTests.hs but there should be no reason to.) Your Task Ensure that your code is written in good Haskell style. Technical Report (COMP1100: 25 Marks; COMP1130: 35 Marks) You are to write a concise technical report about your assignment. The maximum word count is 1500 words. This is a limit, not a quota; concise presentation is a virtue. 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 lab time and tutors Your university ID An excellent report will: Demonstrate a 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 the concepts taught in this course. 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 a 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, you could 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 any assumptions that you needed to make, and how they have 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 (i.e. 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? How did you test the quality of your AI’s play? 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 them. Try to 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. 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 VSCodium. 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 Recounting 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 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 and do not share detailed descriptions of your design. 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. Course staff will not look at assignment code unless it is posted privately in Piazza, or in a drop-in consultation. Course staff will typically give assistance by asking questions, 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. We will help you get unstuck by asking questions and pointing you to relevant lecture and lab material. You will receive feedback on you 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 by using your browser to visit https://gitlab.cecs.anu.edu.au/uXXXXXXX/assignment3, where XXXXXXX is your student number. Ensure your program compiles and runs, including the cabal v2-test test suite. 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 successfully added it in GitLab. Updated: 07 Jun 2022 / Responsible Officer: Director, School of Computing / Page Contact: Course Convenor Contact ANU Copyright Disclaimer Privacy Freedom of Information +61 2 6125 5111 The Australian National University, Canberra CRICOS Provider : 00120C ABN : 52 234 063 906 You appear to be using Internet Explorer 7, or have compatibility view turned on. Your browser is not supported by ANU web styles. » Learn how to fix this » Ignore this warning in future
