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COMP90054 AI Planning for Autonomy
Creation date：2024-04-07 15:47:42
Belonging category：计算机computer science
Planning for Autonomy

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COMP90054 AI Planning for Autonomy
Project 1, 2021
Deadline: Thursday 1st April 18:00
This project counts towards 10% of the marks for this subject.
This project must be done individually.
Aims
The aim of this project is to improve your understanding of various search algorithms using
the Berkely Pac Man framework.


Getting started
Before starting the assignment you must do the following:

will create your personal assignment repository on github.
ˆ Clone your assignment repository to your local machine. The repository contains the
framework that you will need in order to complete the assignment.
ˆ
id=z_NbDvQft0aRdlLFOMIqTTh0m8tXIJpNq6rJJHkp74NUREM3UFhNVjlNMDIwVjNJMUIzRlNNRzZUUS4u
This allows us to link your University ID to your github ID so that we can mark your
assignment.
1
Practice Task (0 marks)
To familiarise yourself with basic search algorithms and the Pacman environment, it is a good
start to implement the breadth first search alrogithm at https://inst.eecs.berkeley.edu/
~cs188/sp21/project1/; however, there is no requirement to do so. To help you understand
how to interact with the framework, I have provided an implementation of the depth first
search algorithm.
Part 1 (1 mark)
Implement the A* Algorithm described in lectures. You should be able to test the algorithm
using the following command:
python pacman.py -l bigMaze -z .5 -p SearchAgent -a fn=astar,heuristic=manhattanHeuristic
Other layouts are available in the layouts directory, and you can easily create you own!
Part 2 (3 marks)
The recursive best first search algorithm is designed to enable optimal solutions to be found
while using less memory than A*. It may be used to find optimal solutions for memory-
constrained problems. The algorithm is defined on page 99 of [2] (which you can access from
the University library and is linked to in the Week 1 and 2 modules on Canvas) and repro-
duced here for your convenience:
Note that this is a tree-search algorithm which does not consider repeated states. For exam-
ple, moving Pacman down and then up again would produce a new state. As a result, the
algorithm will expand a large number of search nodes in order to find solutions to problems.
I encourage you to consider how you could modify the algorithm to reduce the number of
nodes generated without requiring too much memory, but for the purposes of this question
2
you should implement the algorithm as described. You should be able to test the algorithm
using the following command:
python pacman.py -l tinyMaze -p SearchAgent -a fn=rebfs
Other layouts are available in the layouts directory, and you can easily create you own!
Part 3 (4 marks)
We now consider a slight change to the rules of Pacman, specifically allowing non-uniform
action costs. For the purposes of this question, moving Pacman to a square that is empty or
contains food has a cost of 1, while moving Pacman to a square that contains a Capsule has
a cost of 0. Note that once Pacman eats the capsule the square becomes empty so moving
Pacman back to that square would incur a cost of 1.
We wish to solve the problem of eating all the food in the maze in as few steps as possible.
For this, we’ll need a new search problem definition which formalizes the food-clearing prob-
lem: FoodSearchProblem in searchAgents.py (implemented for you). A solution is defined to
be a path that collects all of the food in the Pacman world. You should already be able to
solve this problem using your A* search implementation with the null Heuristic, but you will
find that heuristic quite inefficient. As a reference, our implementation expands over 100,000
node to find a solution of length 25 for task3search.
Your task is to implement foodHeuristic in order to improve the efficiency of A* search
for this problem. Recall that in order to find optimal solutions to the problem, your heuristic
must be admissible.
Non-Trivial Heuristics: The trivial heuristics are the ones that return zero everywhere
(UCS) and the heuristic which computes the true completion cost. The former won’t save
you any time, while the latter will timeout the autograder. You want a heuristic which re-
duces total compute time, though for this assignment the autograder will only check node
counts (aside from enforcing a reasonable time limit).
Grading: Your heuristic must be a non-trivial non-negative admissible heuristic to receive
any points. Make sure that your heuristic returns 0 at every goal state and never returns a
negative value. Depending on how few nodes your heuristic expands on task3search, you’ll
be graded:
Number of nodes expanded Grade
Less than 20,000 1/4
Less than 10,000 2/4
Less than 5,000 3/4
Less than 2,500 4/4
You can check the performance of your algorithm by running the following command:
python pacman.py -l task3Search -p AStarFoodSearchAgent
Since this computation can take some time for less efficienct heuristics, you may wish to start
by verifying that your heuristic finds an optimal solution for task3Small and task3Medium.
3
Part 4 (2 marks)
Challenge Question
Note that this is a much more difficult question that requires you to interpret and
implement an algorithm from a research paper. Learning to implement it suc-
cessfully will give you great experience in solving a modern AI planning problem
and experience in self-directed learning – something that is valuable in general,
but particularly with contemporary AI techniques, but is not necessary in order
to do well in the subject. As a result there is only a small mark allocation for
this question.
Deceptive path-planning involves finding a path to a goal that makes it difficult for an outside
observer to guess what that goal might be. This paper by Masters and Sardina describes a
number of algorithms for deceptive path-planning [1]. The main idea is that an agent has a
true goal as well as one or more false goals. The agent plans a path to the true goal designed
to make it difficult for an observer to figure out whether it is trying to reach the true goal or
one of the false goals.
Your task is to implement two of the strategies described in [1] using the Pacman frame-
work. For each of the deceptive path planning layouts, assume that the true goal is represented
by the Food and that the false goals are represented by Capsules.
Case category
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SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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