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Homework 2 of CS 165A

提供高质量的essay代写,Paper代写,留学作业代写-天才代写 Homework 2 of CS 165A (Fall 2020)

• Assigned on Feb 4, 2021 (Thursday) Due at 11:59 pm on March 9, 2021 (Tuesday)
• Be sure to read ”Policy on Academic Integrity” on the course syllabus
• Any updates or correction will be posted on the course Announcements page and piazza, so check
there occasionally
• You must do your own work independently. We will use software to automatically detect any
plagiarisms.
• TA in charge of this homework: Kaiqi Zhang (kzhang70@ucsb.edu)
Search and Games
Introduction
In This project, you will design agents for the classic version of Pacman, including ghosts. Along
the way, you will implement both reflex agents and minimax search and try your hand at evaluation
function design.
Before you start programming, consider how to formulate the PAC-man into a artifical intelligence
problem, i.e., what are the actions you are supposed to make? How many states are there in total?
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How are the actions leading to transitioning of the states?
We suggest you using python3 (python3.6, python3.7) for the project. If you want to use C++ for
programming, please read (https://docs.python.org/3/extending/extending.html) on how to write an
Python module using C++. Please talk with me so that I can set up autograder for you.
The code for this project consists of several Python files, some of which you will need to read and
understand in order to complete the assignment, and some of which you can ignore. You can download
and unzip all the code and supporting files from [search_and_games.zip]
Files you will edit.
multiAgents.py Where your search-based agents will reside. Please modify the MultiPacmanAgent
class.
Files you might want to look at
pacman.py The main file that runs Pacman games. This file describes a Pacman GameState type,
which you use in this project.
game.py The logic behind how the Pacman world works. This file describes several supporting types
like AgentState, Agent, Direction, and Grid.
util.py . Useful data structures for implementing search algorithms.
Files you will not edit
agentTestClasses.py Specific autograding test classes graphicsDisplay.py Graphics for Pacman
graphicsUtils.pySupport for Pacman graphics textDisplay.pyASCII graphics for Pacman
ghostAgents.pyAgents to control ghosts keyboardAgents.pyKeyboard interfaces to control Pacman
layout.py Code for reading layout files and storing their contents autograder.py Project autograder
testParser.pyParses autograder test and solution files testClasses.pyGeneral autograding test
classes test_cases/Directory containing the test cases for each question
Files to Edit and Submit: You will fill in portions of multiAgents.py, during the assignment.
You should submit this file with your code and comments. Please do not change the other files in this
distribution or submit any of our original files other than this file.
Evaluation: Your code will be autograded for technical correctness. Please do not change the names of
any provided functions or classes within the code, or you will wreak havoc on the autograder. However,
the correctness of your implementation – not the autograder’s judgements – will be the final judge of
your score. If necessary, we will review and grade assignments individually to ensure that you receive
due credit for your work.
Academic Dishonesty: We will be checking your code against other submissions in the class for
logical redundancy. If you copy someone else’s code and submit it with minor changes, we will know.
These cheat detectors are quite hard to fool, so please don’t try. We trust you all to submit your own
work only; please don’t let us down. If you do, we will pursue the strongest consequences available to
us.
Getting Help: You are not alone! If you find yourself stuck on something, contact the course staff
for help. Office hours, recitation, and Piazza are there for your support; please use them. If you can’t
make our office hours, let us know and we will schedule more. We want these projects to be rewarding
and instructional, not frustrating and demoralizing. But, we don’t know when or how to help unless
you ask.
This assignment is based on the Pacman AI projects developed at UC Berkeley, http://ai.berkeley.edu.
Welcome to Pacman
After downloading the code ([search_and_games.zip]), unzipping it, and changing to the directory, you
should be able to play a game of Pacman by typing the following at the command line:
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python pacman.py
Pacman lives in a shiny blue world of twisting corridors and tasty round treats. Navigating this world
efficiently will be Pacman’s first step in mastering his domain.
The simplest agent in searchAgents.py is called the GoWestAgent, which always goes West (a triv?ial reflex agent). This agent can occasionally win: python pacman.py –layout testMaze –pacman
GoWestAgent
But, things get ugly for this agent when turning is required: python pacman.py –layout tinyMaze
–pacman GoWestAgent
If Pacman gets stuck, you can exit the game by typing CTRL-c into your terminal. Soon, your agent
will solve not only tinyMaze, but any maze you want. Note that pacman.py supports a number of
options that can each be expressed in a long way (e.g., --layout) or a short way (e.g., -l). You can
see the list of all options and their default values via: python pacmen.py -h
Note: if you get error messages regarding Tkinter, see this page
In this assignment, You need to implement a search agent for pacman. There can be more than one
pacman in the maze. In this case pacmans need to both collaborate and compete against each other:
their scores are counted separately, but as long as one pacman is dead, the game ends. You’re free to
use any search algorithm (minimax, expectimax, A*, etc.), and design your heuristic if needed. The
only requirement is that the search depth must not exceed the parameter given to you. A search depth
of 1 means every agent (pacman and ghost) in this maze move one step, and a search depth of 2 means
every agent (pacman and ghost) in this maze move alteratively and each agent move two steps, etc.
The pacmans will always move first. If your search depth exceed that requirement, you’ll submission
will be void and you’ll get 0 credit for Gameplay performance part.
You can run python pacman.py -p MultiPacmanAgent -a depth=3 -g DirectionalGhost locally
to see how your search agent works.
Grading
Grade Breakdown:
• 20% Complete Report
• 10% includes Makefile and/or executable that matches specification.
• 10% accepts command line arguments; produces required outputs; no segfaults, exceptions, or
logic bugs during gameplay
• 60% Gameplay performance
Plagiarism Warning: We are going to use software to check plagiarism. You are expected to finish the
project by yourself without using any code from others.
Your pacman will be evaluated in a medium mazes for 10 times. It will be the only pacman in this maze,
together with a few ghosts rushing to you with probability 0.5 and random walking with probability
0.5. As long as you win at least once (eating all the foods), you get 60% of the credits for gameplay
performance part. If you get more than 1500 scores on average, every 10 score worth 1% of the credit
in this part. The extra credit will be based upon the leaderboard and tournament.
Leader board
Your pacman will be evaluated in a set of different mazes for 10 times. and the total score will be used
in the leaderboard. The runtime will be recorded as well. If there is a tie in scores, the one with the
least runtime will take the lead. Top 3 in the leaderboard will get extra credit.
Tournaments
Participation in the Tournaments is optional but can gain additional credit (and fame!). Due to the
limitation of gradescope, tournament results can only be hold offline. If you want to participant, please
include a text file named TOURNAMENT. The tournament will be held weekly after getting at least 3 valid
submissions that would like to participant, and the result will be announed on Piazza.
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Submission
Complete as specified in the project instructions. Then upload search.py to Gradescope.
We will evaluate your code with a variety of test cases. Those test cases on the gradescape are not
restricted to those that are provided to you, please pay attention to corner cases. The hard-coded
solutions will not work on gradescope and we will use software to automatically detect any plagiarisms.
The autograder on Gradescope might take a while but don’t worry: so long as you submit before
the due date, it’s not late. 4
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