题意：
通过一个吃豆子游戏的理解各种搜索算法
解析：
在这个项目中，您的Pacman代理人将通过他的迷宫世界找到路径，既可以到达特定的位置，也可以有效地收集食物。您将构建通用搜索算法并将其应用于Pacman场景。
游戏场景截图
part1：实现深度搜索算法
先确定当前节点，依次从当前节点的未被访问的邻接点出发，对图进行深度优先遍历
part1 code
part2：Weighted A*搜索
计算每个候选节点的代价值，代价值为起点到当前节点的cost加上当前节点到目的节点的heuristic值，从中选取最小的，直到达到目标状态。
part2 code
part3：创建一个能够吃掉迷宫中所有豆子的代理人
构建CapsuleSearchAgent类，进行两步搜索，第一步目标状态设为Capsule，采用wasearch，第二部目标状态设为所有food，采用wasearch。
part3 code
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The University of Melbourne School of Computing and Information Systems
COMP90054 AI Planning for Autonomy
Project 1, 2019
Deadline: Monday 2 September 18:00
This project counts towards 10% of the marks for this subject.
This project must be done individually.
Aims
The aims of this project are to improve your understanding of the various search algorithmsand to experience how to derive heuristics, using the Berkely Pac Man framework.
https://inst.eecs.berkeley.edu/~cs188/fa18/project1.html
Your task
Your tasks relate to the assignment at https://inst.eecs.berkeley.edu/~cs188/fa18/project1.html.
Practice Task (0 marks)
To familiarise yourself with basic search algorithms and the Pacman environment, it is a good start to implement the tasks at https://inst.eecs.berkeley.edu/~cs188/fa18/project1.html, especially the first four tasks; however, there is no requirement to do so.
Part 1 (2 marks)
Implement the Iterative Deepening Search algorithm discussed in lectures. You should be able to test the algorithm using the following command:
python pacman.py -l mediumMaze -p SearchAgent -a fn=ids
Other layouts are available in the layouts directory, and you can easily create you own!
Part 2 (2 marks)
Implement the Weighted A* algorithm discussed in lectures using W = 2. You may hardcode this weight into your algorithm (that is, do not pass as a parameter).
You should be able to call your function using the fn=wastar parameter from the command line, i.e. you should be able to test the algorithm using the following command:
python pacman.py -l bigMaze -z .5 -p SearchAgent -a fn=wastar,heuristic=manhattanHeuristic
Other layouts are available in the layouts directory, and you can easily create you own!
Part 3 (6 marks)
Now we wish to solve a more complicated problem. Just like in Q7 of the Berkerley PacMan framework, we woud like to create an agent that will eat all of the dots in a maze.Before doing so, however, the agent must eat a Capsule that is present in the maze. Your code should ensure that no food is eaten before the Capsule. You can assume that there is always exactly one Capsule in the maze, and that there will always be at least one path from Pacman’s starting point to the capsule that doesn’t pass through any food.
In order to implement this, you should create a new problem called CapsuleSearchProblem and a new agent called CapsuleSearchAgent. You will also need to implement a suitable foodHeuristic. You may choose to implement other helper classes/functions. You should be able to test your program by running the following code:
python pacman.py -l capsuleSearch -p CapsuleSearchAgent -a fn=wastar,prob=CapsuleSearchProblem,heuristic=foodHeuristic
An agent that eats the capsule then proceeds to eat all of the food on the maze will receive 3 marks. The remaining 3 marks will be based on the performance of your agent (i.e. number of nodes expanded), as in Q7 of the Berkeley problem. Since you are using the Weighted A* algorithm, however, the number of node expansions required for each grade will vary.
HINT: Think carefully about how you intend to structure your solution before starting to implement it.
NOTE: You should not change any files other than search.py and searchAgents.py. You should not import any additional libraries into your code. This risks being incompatible with our marking scripts.
Marking criteria
This assignment is worth 10% of your overall grade for this subject. Marks are allocated according to the breakdown listed above, based on how many of our tests the algorithms pass. No marks will be given for code formatting, etc.
Submission
The master branch on your repository will be cloned at the due date and time. From this repository, we will copy only the files: search.py and searchAgents.py. Do not change any other file as part of your solution, or it will not run. Breaking these instructions breaks our marking scripts, delays marks being returned, and more importantly, gives us a headache.
Note: Submissions that fail to follow the above will be penalised.
Academic Misconduct
The University misconduct policy1 applies. Students are encouraged to discuss the assignment topics, but all submitted work must represent the individuals understanding of the topic. The subject staff take academic misconduct seriously. In the past, we have prosecuted several students that have breached the university policy. Often this results in receiving 0 marks for the assessment, and in some cases, has resulted in failure of the subject.
Important: As part of marking, we run all submissions via a code similarity comparison tool. These tools are quite sophisticated and are not easily fooled by attempts to make code look different. In short, if you copy code from classmates or from online sources, you risk facing academic misconduct charges.
But more importantly, the point of this assignment is to have you work through a series of foundational search algorithms. Successfully completing this assignment will make the rest of the subject, including other assessment, much smoother for you. If you cannot work out solutions for this assignment, submitting another persons code will not help in the long run.
See https://academichonesty.unimelb.edu.au/policy.html
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