Description
- Introduction
In this assignment, your Pacman agent will nd paths through his maze world, both to reach a particular location and to collect food e ciently. You will build general search algorithms and apply them to Pacman scenarios.
All those colored walls,
Mazes give Pacman the blues,
So teach him to search.
As in Assignment 0, this assignment includes an autograder for you to grade your answers on your machine.
This can be run with the command:
python2 autograder.py
Note: other tests will be run on your submitted code beyond the tests the autograder runs.
See the autograder tutorial in Assignment 0 for more information about using the autograder.
The code for this assignment consists of several Python les, 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 all the code and supporting les as a zip archive. In that zip you will nd the following les:
Files you’ll edit:
search.py Where all of your search algorithms will reside.
searchAgents.py Where all of your search-based agents will reside.
Files you might want to look at:
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pacman.py The main le that runs Pacman games.
This le describes a Pacman GameState type, which you use in this assignment.
game.py The logic behind how the Pacman world works.
This le describes several supporting types like AgentState, Agent, Direction, and Grid.
util.py Useful data structures for implementing search algorithms.
Supporting les you can ignore:
graphicsDisplay.py Graphics for Pacman
graphicsUtils.py Support for Pacman graphics
textDisplay.py ASCII graphics for Pacman
ghostAgents.py Agents to control ghosts
keyboardAgents.py Keyboard interfaces to control Pacman
layout.py Code for reading layout les and storing their contents
autograder.py Assignment autograder
testParser.py Parses autograder test and solution les
testClasses.py General autograding test classes
test_cases/ Directory containing some test cases for each question
searchTestClasses.py Assignment 1 speci c autograding test classes
Files to Edit and Submit: You will ll in portions of search.py and searchAgents.py during the assign-ment. You may also add other functions and code to these les so as to create a modular implementation. You will submit these les with your modi cations. Please do not change the other les in this distribution or submit any other les other than these les. Note that all of the code that your implementation depends on must reside inside of one of the two submitted les.
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. We will also run some additional tests on your code, in addition to tests supplied in the zip le. If all checks out with your code you will receive all of the points indicated by the autograder, and some more for the additional tests.
Getting Help: You are not alone! If you nd yourself stuck on something, contact us for help. The piazza discussion forum will be monitored and questions answered, and you can also ask questions about the assignment during o ce hours. Also tutorial 1 is dedicated to this assignment. If you can’t make our o ce hours, let us know and we will arrange a di erent appointment. We want the assignment to be rewarding and instructional, not frustrating and demoralizing. But, we don’t know when or how to help unless you ask.
Piazza Discussion: Please be careful not to post spoilers.
What to Submit
You will be using MarkUs to submit your assignment. MarkUs accounts for the course will be soon be set up. You will submit three les:
Your modi ed search.py
Your modi ed searchAgents.py
A signed copy of the following acknowledgment
Note: In the various parts below we ask a number of questions. You do not have to hand in answers to these questions, rather these questions are designed to help you understand what is going on with search.
Welcome to Pacman
After downloading the code search, 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
Note: if python2.7 is not the default python on the machine you are using you will have to change python to the command that invokes python2.7. You can also con gure you IDE (if you use one) to invoke python2.7.
Pacman lives in a shiny blue world of twisting corridors and tasty round treats. Navigating this world e ciently will be Pacman’s rst step in mastering his domain.
The simplest agent in searchAgents.py is called the GoWestAgent, which always goes West (a trivial re ex 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 pacman.py -h
Also, all of the commands that appear in this assignment also appear in commands.txt, for easy copying and pasting. In UNIX/Mac OS X, you can even run all these commands in order with bash commands.txt. (Note if python2.7 is not the default on your machine you might have to change commands in this le).
Note: if you have a non-graphics connection you can run the pacman.py code with the -t argument. If you install python2.7 on your personal machine it should come with the correct graphics support, and the graphics should also work if you are connected to the teaching labs with an X-windows connection.
Question 1 (3 points): Finding a Fixed Food Dot using Depth First Search
In searchAgents.py, you’ll nd a fully implemented SearchAgent, which plans out a path through Pacman’s world and then executes that path step-by-step. The search algorithms for formulating a plan are not implemented { that’s your job.
First, test that the SearchAgent is working correctly by running:
python pacman.py -l tinyMaze -p SearchAgent -a fn=tinyMazeSearch
The command above tells the SearchAgent to use tinyMazeSearch as its search algorithm, which is imple-mented in search.py. Pacman should navigate the maze successfully.
Now it’s time to write full- edged generic search functions to help Pacman plan routes! Pseudocode for the search algorithms you’ll write can be found in the lecture slides. Remember that a search node must contain not only a state but also the information necessary to reconstruct the path (plan) which gets to that state.
Important note: All of your search functions need to return a list of actions that will lead the agent from the start to the goal. These actions all have to be legal moves (valid directions, no moving through walls).
Important note: Make sure to use the Stack, Queue and PriorityQueue data structures provided to you in util.py! These data structure implementations have particular properties which are required for compatibility with the autograder.
Hint: Each algorithm is very similar. Algorithms for DFS, BFS, UCS, and A* di er only in the details of how OPEN is managed. So, concentrate on getting DFS right and the rest should be relatively straightforward.
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Indeed, one possible implementation requires only a single generic search method which is con gured with an algorithm-speci c queuing strategy. (Your implementation need not be of this form to receive full credit).
Implement the depth- rst search (DFS) algorithm in the depthFirstSearch function in |search.py|. To ensure that DFS does not run around in circles, implement path checking to prune cyclic paths during search. Do not use full cycle checking for DFS (we will use full cycle checking for the other searches).
Your code should quickly nd a solution for:
python pacman.py -l tinyMaze -p SearchAgent
python pacman.py -l mediumMaze -p SearchAgent
python pacman.py -l bigMaze -z .5 -p SearchAgent
The Pacman board will show an overlay of the states explored, and the order in which they were explored (brighter red means earlier exploration). Check that the exploration order is what you would have expected? Does Pacman actually go to all the explored squares on his way to the goal?
Hint: If you use a Stack as your data structure, the solution found by your DFS algorithm for medium-Maze should have a length of 130 (provided you push successors onto the fringe in the order provided by getSuccessors; you might get 246 if you push them in the reverse order). Is this a least cost solution? If not, think about what depth- rst search is doing wrong.
Question 2 (3 points): Breadth First Search
Implement the breadth- rst search (BFS) algorithm in the breadthFirstSearch function in search.py. This time you must implement full cycle checking in your search algorithm to avoid the overhead of cyclic paths. Test your code the same way you did for depth- rst search.
python pacman.py -l mediumMaze -p SearchAgent -a fn=bfs python pacman.py -l bigMaze -p SearchAgent -a fn=bfs -z .5
Does BFS nd a least cost solution? If not, check your implementation.
Hint: If Pacman moves too slowly for you, try the option –frameTime 0.
Note: If you’ve written your search code generically, your code should work equally well for the eight-puzzle search problem without any changes.
python eightpuzzle.py
Question 3 (3 points): Varying the Cost Function
While BFS will nd a fewest-actions path to the goal, we might want to nd paths that are “best” in other senses. Consider mediumDottedMaze and mediumScaryMaze.
By changing the cost function, we can encourage Pacman to nd di erent paths. For example, we can charge more for dangerous steps in ghost-ridden areas or less for steps in food-rich areas, and a rational Pacman agent should adjust its behavior in response.
Implement the uniform-cost search algorithm with full cycle checking in the uniformCostSearch function in search.py. We encourage you to look through util.py for some data structures that may be useful in your implementation. You should now observe successful behavior in all three of the following layouts, where the agents below are all UCS agents that di er only in the cost function they use (the agents and cost functions are written for you):
python pacman.py -l mediumMaze -p SearchAgent -a fn=ucs python pacman.py -l mediumDottedMaze -p StayEastSearchAgent python pacman.py -l mediumScaryMaze -p StayWestSearchAgent
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Note: You should get very low and very high path costs for the StayEastSearchAgent and StayWestSearchAgent respectively, due to their exponential cost functions (see searchAgents.py for details).
Question 4 (3 points): A* search
Implement A* search with full cycle checking in the empty function aStarSearch in search.py. A* takes a heuristic function as an argument. Heuristics take two arguments: a state in the search problem (the main argument), and the problem itself (for reference information). The nullHeuristic heuristic function in search.py is a trivial example.
You can test your A* implementation on the original problem of nding a path through a maze to a xed position using the Manhattan distance heuristic (implemented already as manhattanHeuristic in searchAgents.py).
python pacman.py -l bigMaze -z .5 -p SearchAgent -a fn=astar,heuristic=manhattanHeuristic
You should see that A* nds the optimal solution slightly faster than uniform cost search (about 549 vs. 620 search nodes expanded in our implementation, but ties in priority may make your numbers di er slightly).
What happens on openMaze for the various search strategies?
You will nd that DFS will take too long on openMaze maze since it only does path-checking. If you use full cycle checking with DFS it will nd a solution (a very unusual one!).
Question 5 (3 points): Finding All the Corners
The real power of A* will only be apparent with a more challenging search problem. Now, it’s time to formulate a new problem and design a heuristic for it.
In corner mazes, there are four dots, one in each corner. Our new search problem is to nd the shortest path through the maze that touches all four corners (whether the maze actually has food there or not). Note that for some mazes like tinyCorners, the shortest path does not always go to the closest food rst! Hint: the shortest path through tinyCorners takes 28 steps.
Note: Make sure to complete Question 2 before working on Question 5, because Question 5 builds upon your answer for Question 2.
Implement the CornersProblem search problem in searchAgents.py. You will need to choose a state representation that encodes all the information necessary to detect whether all four corners have been reached. Now, your search agent should solve:
python pacman.py -l tinyCorners -p SearchAgent -a fn=bfs,prob=CornersProblem python pacman.py -l mediumCorners -p SearchAgent -a fn=bfs,prob=CornersProblem
To receive full credit, you need to de ne an abstract state representation that does not encode irrelevant information (like the position of ghosts, where extra food is, etc.). In particular, do not use a Pacman GameState as a search state. Your code will be very, very slow if you do (and also wrong).
Hint: The only parts of the game state you need to reference in your implementation are the starting Pacman position and the location of the four corners.
Our implementation of breadthFirstSearch expands just under 2000 search nodes on mediumCorners.
However, heuristics (used with A* search) can reduce the amount of searching required.
Question 6 (3 points): Corners Problem: Heuristic
Note: Make sure to complete Question 4 before working on Question 6, because Question 6 builds upon your answer for Question 4.
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Implement a non-trivial, admissible heuristic for the CornersProblem in cornersHeuristic.
python pacman.py -l mediumCorners -p AStarCornersAgent -z 0.5
Note: AStarCornersAgent is a shortcut for
-p SearchAgent -a fn=aStarSearch,prob=CornersProblem,heuristic=cornersHeuristic.
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 reduces 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, you’ll be graded:
| Number of nodes expanded | Grade | |
| more than 2000 | 0/3 | |
| at most 2000 | 1/3 | |
| at most 1600 | 2/3 | |
| at most 1200 | 3/3 | |
Remember: If your heuristic is inadmissible, you will receive no credit, so be careful!
Question 7 (4 points): Eating All The Dots
Now we’ll solve a hard search problem: eating all the Pacman food in as few steps as possible. For this, we’ll need a new search problem de nition which formalizes the food-clearing problem: FoodSearchProblem in searchAgents.py (implemented for you). A solution is de ned to be a path that collects all of the food in the Pacman world. For the present assignment, solutions do not take into account any ghosts or power pellets; solutions only depend on the placement of walls, regular food and Pacman. (Of course ghosts can ruin the execution of a solution! We’ll get to that in the next assignment.) If you have written your general search methods correctly, A* with a null heuristic (equivalent to uniform-cost search) should quickly nd an optimal solution to testSearch with no code change on your part (total cost of 7).
python pacman.py -l testSearch -p AStarFoodSearchAgent
Note: AStarFoodSearchAgent is a shortcut for
-p SearchAgent -a fn=astar,prob=FoodSearchProblem,heuristic=foodHeuristic’
You should nd that UCS starts to slow down even for the seemingly simple tinySearch. As a reference, our implementation takes 2.5 seconds to nd a path of length 27 after expanding 5057 search nodes.
Note: Make sure to complete Question 4 before working on Question 7, because Question 7 builds upon your answer for Question 4.
Fill in foodHeuristic in searchAgents.py with an admissible heuristic for the FoodSearchProblem. Try your agent on the trickySearch board:
python pacman.py -l trickySearch -p AStarFoodSearchAgent
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Our UCS agent nds the optimal solution in about 13 seconds, exploring over 16,000 nodes.
Any non-trivial non-negative admissible heuristic will receive 1 point. 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, you’ll get additional points:
| Number of nodes expanded | Grade | |
| more than 15000 | 1/4 | |
| at most 15000 | 2/4 | |
| at most 12000 | 3/4 | |
| at most 9000 | 4/4 (full credit; medium) | |
| at most 7000 | 5/4 (optional extra credit; hard) | |
Remember: If your heuristic is inadmissible, you will receive no credit, so be careful! Can you solve medi-umSearch in a short time? If so, we’re either very, very impressed, or your heuristic is inadmissible.
Academic Honesty
We are aware that solutions to the original Berkeley project exist on the internet. Do not use these solutions as this would be plagiarism. To earn marks on this assignment you must develop your own solutions. Also please consider the following points.
You are to implement the search algorithms presented in the course. These algorithms di er in subtle but important ways from other presentations of this material. If you implement your search based on other non-course material it might give the wrong answers. If you try to use solutions found on the internet the same problem might occur.
We will check for answers that would arise from solutions to the original Berkeley project. Such answers indicate that your solution is incorrect, reproducing the errors of the Berkeley lectures. This will cause you to fail some of our additional tests and you will lose marks for those tests.
If we nd evidence of plagiarism we will investigate thoroughly and we will send your case to the University Academic O enses O ce.
Please do not implement your own “improvement” to the search algorithms: it will wreak havoc with the automarker. (Note, if you have invented a signi cant improvement we would be happy to hear about it, but don’t use it in this assignment).
You will be asked to write, sign, scan, and submit a statement acknowledging that the code you submitted was written by you.
Although the assignment includes an autograder, additional tests will be run on your code after sub-mission.
Working successfully in a pair
You may work in pairs for this assignment.
If you are working with a partner, make sure that you are actually working together. Your goal should be for the two of you to help each other learn the material and to avoid getting stuck with frustrating errors. If you split up the assignment and work separately, you are not getting practice on all aspects of the assignment.
Sometimes a student who is working with a partner drops the course or becomes ill in the middle of an assignment. If this happens, the other partner is still responsible for completing the assignment on time. If he or she has been actively engaged in the entire assignment, this should not be a problem; the assignments are designed so that an individual student can complete them. However, if the remaining partner has not
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been actively involved or does not have copies of all of the work, they will have serious di culty completing the assignment. Make sure you don’t nd yourself in this situation: Be active in all parts of the assignment, and make sure that at the end of each meeting, both partners have a copy of all of the work.
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