Project 1: Search

Table of Contents

Introduction

Welcome

Q1: Depth First Search

Q2: Breadth First Search

Q3: Uniform Cost Search

Q4: A* Search

Q5: Corners Problem: Representation

Q6: Corners Problem: Heuristic

Q7: Eating All The Dots: Heuristic

Q8: Suboptimal Search

Submission

All those colored walls,

Mazes give Pacman the blues,

So teach him to search.

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Introduction

In this project, your Pacman agent will find paths through his maze world, both

to reach a particular location and to collect food efficiently. You will build

general search algorithms and apply them to Pacman scenarios.

As in Project 0, this project includes an autograder for you to grade your

answers on your machine. This can be run with the command:

python autograder.py

See the autograder tutorial in Project 0 for more information about using the

autograder.

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 all the code and supporting files as a

zip archive (search.zip).

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:

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.

Supporting files 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

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keyboardAgents.py Keyboard interfaces to control Pacman

layout.py Code for reading layout files and storing their contents

autograder.py Project autograder

testParser.py Parses autograder test and solution files

testClasses.py General autograding test classes

test_cases/ Directory containing the test cases for each question

searchTestClasses.py Project 1 specific autograding test classes

Files to Edit and Submit: You will fill in portions of search.py and

searchAgents.py during the assignment. You should submit these files with

your code and comments. Please do not change the other files in this

distribution or submit any of our original files other than these files.

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 and the discussion forum 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.

Discussion: Please be careful not to post spoilers.

Welcome to Pacman

After downloading the code ( search.zip (https://inst.eecs.berkeley.edu

/~cs188/fa18/assets/files/search.zip) ), unzipping it, and changing to the

directory, you should be able to play a game of Pacman by typing the following

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at the command line:

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 trivial 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 pacman.py -h

Also, all of the commands that appear in this project 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 .

Question 1 (3 points): Finding a Fixed Food Dot using

Depth First Search

In searchAgents.py , you’ll find 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. As you work through the following questions, you might find it useful to

refer to the object glossary (the second to last tab in the navigation bar above).

First, test that the SearchAgent is working correctly by running:

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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 implemented in search.py . Pacman should navigate the

maze successfully.

Now it’s time to write full-fledged 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*

differ only in the details of how the fringe is managed. So, concentrate on

getting DFS right and the rest should be relatively straightforward. Indeed, one

possible implementation requires only a single generic search method which is

configured with an algorithm-specific queuing strategy. (Your implementation

need not be of this form to receive full credit).

Implement the depth-first search (DFS) algorithm in the depthFirstSearch

function in search.py . To make your algorithm complete, write the graph

search version of DFS, which avoids expanding any already visited states.

Your code should quickly find 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). Is the

exploration order 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 mediumMaze should have a length of 130 (provided you push

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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-first search is doing wrong.

Question 2 (3 points): Breadth First Search

Implement the breadth-first search (BFS) algorithm in the breadthFirstSearch

function in search.py . Again, write a graph search algorithm that avoids

expanding any already visited states. Test your code the same way you did for

depth-first 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 find 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 find a fewest-actions path to the goal, we might want to find

paths that are “best” in other senses. Consider mediumDottedMaze and

mediumScaryMaze .

By changing the cost function, we can encourage Pacman to find different

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 graph search algorithm 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 differ only in the cost function they use

(the agents and cost functions are written for you):

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python pacman.py -l mediumMaze -p SearchAgent -a fn=ucs

python pacman.py -l mediumDottedMaze -p StayEastSearchAgent

python pacman.py -l mediumScaryMaze -p StayWestSearchAgent

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* graph search 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 finding a path

through a maze to a fixed 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* finds 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 differ slightly). What happens on

openMaze for the various search strategies?

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 find 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 first! Hint: the shortest path through tinyCorners takes 28 steps.

Note: Make sure to complete Question 2 before working on Question 5, because

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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 define 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.

Implement a non-trivial, consistent 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

Admissibility vs. Consistency: Remember, heuristics are just functions that

take search states and return numbers that estimate the cost to a nearest goal.

More effective heuristics will return values closer to the actual goal costs. To be

admissible, the heuristic values must be lower bounds on the actual shortest

path cost to the nearest goal (and non-negative). To be consistent, it must

additionally hold that if an action has cost c, then taking that action can only

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cause a drop in heuristic of at most c.

Remember that admissibility isn’t enough to guarantee correctness in graph

search — you need the stronger condition of consistency. However, admissible

heuristics are usually also consistent, especially if they are derived from

problem relaxations. Therefore it is usually easiest to start out by brainstorming

admissible heuristics. Once you have an admissible heuristic that works well,

you can check whether it is indeed consistent, too. The only way to guarantee

consistency is with a proof. However, inconsistency can often be detected by

verifying that for each node you expand, its successor nodes are equal or higher

in in f-value. Moreover, if UCS and A* ever return paths of different lengths,

your heuristic is inconsistent. This stuff is tricky!

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 consistent 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 inconsistent, 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 definition which

formalizes the food-clearing problem: 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. For the present project, 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 project.) If you have written your general

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search methods correctly, A* with a null heuristic (equivalent to uniform-cost

search) should quickly find 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 find that UCS starts to slow down even for the seemingly simple

tinySearch . As a reference, our implementation takes 2.5 seconds to find 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 a consistent heuristic for the

FoodSearchProblem . Try your agent on the trickySearch board:

python pacman.py -l trickySearch -p AStarFoodSearchAgent

Our UCS agent finds the optimal solution in about 13 seconds, exploring over

16,000 nodes.

Any non-trivial non-negative consistent 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 inconsistent, you will receive no credit, so be

careful! Can you solve mediumSearch in a short time? If so, we’re either very,

very impressed, or your heuristic is inconsistent.

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Question 8 (3 points): Suboptimal Search

Sometimes, even with A* and a good heuristic, finding the optimal path through

all the dots is hard. In these cases, we’d still like to find a reasonably good path,

quickly. In this section, you’ll write an agent that always greedily eats the

closest dot. ClosestDotSearchAgent is implemented for you in searchAgents.py ,

but it’s missing a key function that finds a path to the closest dot.

Implement the function findPathToClosestDot in searchAgents.py . Our agent

solves this maze (suboptimally!) in under a second with a path cost of 350:

python pacman.py -l bigSearch -p ClosestDotSearchAgent -z .5

Hint: The quickest way to complete findPathToClosestDot is to fill in the

AnyFoodSearchProblem , which is missing its goal test. Then, solve that problem

with an appropriate search function. The solution should be very short!

Your ClosestDotSearchAgent won’t always find the shortest possible path

through the maze. Make sure you understand why and try to come up with a

small example where repeatedly going to the closest dot does not result in

finding the shortest path for eating all the dots.

Submission

In order to submit your project, please upload the following files to Canvas:

search.py and searchAgents.py . Please do not upload the files in a zip file or a

directory.

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Sale!