墨尔本大学COMP90054 assignment2课业解析题意:这个项目的目的是实现一个可以参加比赛的吃豆人pacman的自治代理 解析:Pac Man Agent的实现:
可以使用手工编码的决策树来表达特定于Pac Man的行为。可交付部分中提到的技术比if-else规则的决策树更容易地处理不同的规则。如果决定计算一个策略,可以将其保存到一个文件中,在游戏开始时加载它,在每场游戏之前都有15秒的时间来执行预计算。 Pac Man作为PDDL的经典规划
规划的典型应用包括对规划人员的一次或多次调用。实例由前端动态生成,解决方案被解释为可执行指令。吃豆人和幽灵又不同的目标:吃豆人的目标是为了生存而吃所有网格的点,幽灵的目标是杀死吃豆人。假设游戏是回合制的,因此在每一步都会生成一个实例,其中包含当前世界的状态,即网格中的点和幽灵位置。从吃豆人的角度来看,幽灵不会移动,反之亦然,也就是说,环境是静态的。在每一步,planner都会拿出一个计划,吃掉所有的点,同时避免静态幽灵,并计划让幽灵杀死静态吃豆人。pacman引擎对计划的一个简单解释是,只执行计划的第一个动作,忽略其余的动作,并在下一个步骤中调用planner,更新计算幽灵的新位置。知识点:搜索算法,PDDL,决策树,游戏理论更多可加微信讨论微信号: yzr5211234pdfCOMP90054 AI Planning for Autonomyhttp://ai.berkeley.edu/contest.htmlNote that the Pacman tournament has different rules as it is a two teams game, where your Pacmans become ghostsin certain areas of the grid. Please read carefully the rules of the Pacman tournament. Understanding it well anddesigning a controller for it is part of the expectations. To help you develop your solution you must provide:(i) A working Pac Man Agent that is capable of playing Pac-Man and competing in the tournament. Your Agentcan use any technique, or combination of techniques, that you choose. For example using a classical offthe-shelf planner, Reinforcement Learning, Heuristic Search, Monte Carlo Tree Search or a purpose builtdecision tree of your own making (24 marks).(ii) A recorded 5-minute oral presentation that outlines the theoretical or experimental basis for the design ofyour agents (i.e. why you did what you did), challenges faced, and what you would do differently if you hadmore time. Your presentation must end with a live demo of your different implementations, i.e. showing howthe different techniques your tried work. The video will be shared with us through an unlisted youtube linkin the Wiki of your GitLab repository. (10 marks).(iii) A WIKI: describing the approaches implemented, a small table comparing your different agents showingtheir performance in several scenarios. Discuss briefly the table. The link for the recorded oral-presentationshould be included in the wiki. (6 marks).This project follows up from your Search agents for project 1. You should be familiarized with the pacmanenvironment, and you can use variations of your search agents as a starting point for this project.Corrections: From time to time, students or staff find errors (e.g., typos, unclear instructions, etc.) in theassignment specification. In that case, corrected version will be uploaded to the course LMS as quickly as possibleand an announcement will posted in the course LMS and also to the forum (if they issue was related to a forummessage). The date of the latest specification can be found in the bottom right of each page (S2 2019- Date).Silent Policy: A silent policy will take effect 48 hours before this assignment is due. This means that noquestion about this assignment will be answered, whether it is asked on the newsgroup, by email, or in person.Team Registration & GitLab Repo SetupThis is a group/team project assignment (groups of 3 or 4 students). There will be no need to explicitly submitanything for this assignment project, except filling the certification, as the repository on GitLab will represent theteam’s submission (more details below).COMP90054 AI Planning for Autonomy 1 S2 2019- September 15, 2019A team should do the following (only one student member needs to do these steps):1. Set-up project GIT repository:• One of the members of the new team should fork privately the following template project repositoryin UoM GitLab using their Unimelb student account:https://gitlab.eng.unimelb.edu.au/nlipovetzky/comp90054-pacmanThis repository has the initial template for the contest under subdirectory pacman-contest/. Make sure:• Click on the gitlab link above and Fork the repository forllowing these instructions.• Repository name must be slightly changed to “comp90054-pacman-
t:• See this guide for tagging using the command line or this video, and here for tagging via GitLabinterface directly.• To re-submit another version you need to delete previous submission tags– First delete it from the GIT server by running: git push –delete origin
t of insights on how your solution is performing (bydownloading and re-playing each game) and how to improve it. Results, including replays for every game,will be available only for those teams that have submitted in https://people.eng.unimelb.edu.au/nlipovetzky/comp90054tournament/.The pre-competitions will run every night at 0:10AM using your latest tagged version. In order to increasethe amount of feedback, we will be running your team against the staff teams every 2hours, from 8AM untill10PM.You can re-submit multiple times, as long as your repository has a submission tag, and they carry no marking at all; they are just for debugging and improving your solution! You do not need to certify the preliminarysubmissions, only the final one (you do need to register your team though).Marking criteriaA final contest using many layouts will be run juts after final submission. The top-8 will play quarterfinals, semifinals and finals, time permitting live in the last day of class or in week 13 in a day specified for that (these finalphases will not be part of the marking criteria, just bonus marks).The final contest and the quality of the Wiki and Presentation will be used to derive the final marks for theproject.Part (i) Pacman Agent– 24 marksMarks will be given according to final position in the tournament with respect to staffTeam:• Pacman competitor finishes above the staffTeamBasic agent [9 marks].• Pacman competitor finishes above the staffTeamMedium agent [6 marks].• Pacman competitor finishes above the staffTeamTop agent [6 marks].• Pacman competitor finishes above the staffTeamSuper agent [3 marks].• Final competition place (up to 2 bonus marks).– Winner team of each league in the preliminary competition will receive 1 bonus mark.– Winner of the Final Tournament will receive 1 bonus mark.The precise mark will depend how far your agent system is from these reference agents in the final contest.COMP90054 AI Planning for Autonomy 5 S2 2019- September 15, 2019Part (ii) Video– 10 marks• A clear presentation of the design decisions made, challenges experienced, and possible improvements [3marks]• A clear demonstration and understanding of the subject material [2 marks]• Demo of the different agents implemented across a variety of scenarios, showcasing pitfalls and benefits ofeach approach. No need of full game demo, just edit interesting parts and explain your insights [5 marks]Part (iii) Wiki– 6 marks• A clear written description of the design decisions made, approaches taken, challenges experienced, andpossible improvements [3 marks]• An experimental section that justifies and explains the performance of the approaches implemented, includinga table and discussion [3 marks]The staffTeams are the reference baselines: the farther an agent is from the base reference agents (Basic,Medium, and Top), the more marks it will attract. The only exception is staffTeamSuper, any team that finishesabove it, will earn full marks (24 points). This together with the quality of the Wiki and the presentation willdetermine the final mark, then adjusted as per individual contribution2 . So this means that if an agent systemis between staffTeamMedium and staffTeamTop and has a VERY GOOD (all marks earned) Wiki andPresentation, then it will score between 77.5% and 92.5% overall (the closer to staffTeamTop, the closer to92.5%).Inter-University CompetitionThe top teams of the tournament will qualify to the yearly championship accross RMIT and The University ofMelbourne, which will run each semester along with the best teams since 2017 onward (given you grant us permission). Note that the top-8, i.e. 1st and 2nd of each of the 4 leagues, will play quarterfinals, semifinals and finals,time permitting live in the last day of class. This is just “for fun” and will attract no marks, but is something thatprevious students have stated in their CVs!https://sites.google.com/view/pacman-capture-hall-fame/I hope you enjoy this open-ended contest-based project and learn much from it. If you still havedoubts about the project and/or this specification do not hesitate asking in the Course Forum.GOOD LUCK!Academic MisconductThe University misconduct policy3 applies. Students are encouraged to discuss the assignment topic, but all submitted work must represent the individual’s understanding of the topic.The subject staff take academic misconduct very seriously. In this subject in the past, we have successfullyprosecuted several students that have breached the university policy. Often this results in receiving 0 marks for theassessment, and in some cases, has resulted in failure of the subject.2We will use the team code repository as a way to gauge any anomalies in the effort put in throughout semester for any student. Thiscould lead to fairly different marks within the same team if deemed necessary. Plagiarism detection software will also be used.3See https://academichonesty.unimelb.edu.au/policy.htmlCOMP90054 AI Planning for Autonomy 6 S2 2019- September 15, 2019