CAP4630/5605 Project 2 – Search in Pacman




5/5 - (1 vote)

All those colored walls,
Mazes give Pacman the blues,
So teach him to search.


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.

This project includes an autograder for you to grade your answers on your machine. This can be
run with the command:

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 ( on Canvas).

Files you’ll edit: Where all of your search algorithms will reside. Where all of your search-based agents will reside.

Files you might want to look at: The main file that runs Pacman games. This file describes a Pacman
GameState type, which you use in this project. The logic behind how the Pacman world works. This file describes several
supporting types like AgentState, Agent, Direction, and Grid. Useful data structures for implementing search algorithms.
Supporting files you can ignore: Graphics for Pacman Support for Pacman graphics ASCII graphics for Pacman Agents to control ghosts Keyboard interfaces to control Pacman Code for reading layout files and storing their contents Project autograder Parses autograder test and solution files General autograding test classes
test_cases/ Directory containing the test cases for each question Project 1 specific autograding test classes

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


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.

Welcome to Pacman

After downloading the code (, 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:

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 is called the GoWestAgent, which always goes West
(a trivial reflex agent).

This agent can occasionally win:
python –layout testMaze –pacman GoWestAgent
But, things get ugly for this agent when turning is required:
python –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 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 -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 (25 points): Finding a Fixed Food Dot using Depth First

In, 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:
python -l tinyMaze -p SearchAgent -a fn=tinyMazeSearch
The command above tells the SearchAgent to use tinyMazeSearch as its search algorithm,
which is implemented in 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! 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, Uniform Cost Search (UCS), and
A* differ only in the details of how the frontier/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 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 -l tinyMaze -p SearchAgent
python -l mediumMaze -p SearchAgent
python -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 successors onto the
frontier/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 (25 points): Breadth First Search

Implement the breadth-first search (BFS) algorithm in the breadthFirstSearch function
in 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 -l mediumMaze -p SearchAgent -a fn=bfs
python -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.

Question 3 (25 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 We encourage you to look through 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):

python -l mediumMaze -p SearchAgent -a fn=ucs
python -l mediumDottedMaze -p StayEastSearchAgent

python -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 for details).

Question 4 (25 points): A* search

Implement A* graph search in the empty function aStarSearch in 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 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
python -l bigMaze -z .5 -p SearchAgent -a

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?