All those colored walls,

Mazes give Pacman the blues,

So teach him to search.

In this assignment, 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.

The code for this assignment 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 (including this description) as a zip archive.

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 assignment. |

`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 |

`keyboardAgents.py` |
Keyboard interfaces to control Pacman |

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

**What to submit:** You will fill in portions of `search.py`

and `searchAgents.py`

during the
assignment. You should submit these two files and a PDF of the proof we
request below in a single zip file via Sakai.

**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 output -- 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:** You may discuss questions with
your classmates, but any written code or text you
submit must be your own work. You may not copy code or text from your
classmates or from the internet.

**Getting Help:** You are not alone! If you find yourself
stuck on something, contact us for help. Office hours 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 assignments to be rewarding
and instructional, not frustrating and demoralizing. However, we don't know
when or how to help unless you ask. One more piece of advice: if you don't
know what a variable does or what kind of values it takes, print it out.

python pacman.pyPacman 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 GoWestAgentThings get ugly for this agent when turning is required:

python pacman.py --layout tinyMaze --pacman GoWestAgentIf 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 -hAlso, 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`

.
`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 need to refer to this glossary of objects in the code.
First, test that the `SearchAgent`

is working correctly by running:
python pacman.py -l tinyMaze -p SearchAgent -a fn=tinyMazeSearchThe 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 and textbook. 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).

*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).

*Hint:* Make sure to check out the `Stack, Queue`

and `PriorityQueue`

types provided to you in `util.py`

!

* Question 1 (10 points) * Implement the depth-first search (DFS) algorithm in the

`depthFirstSearch`

function in `search.py`

. To make your algorithm 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 SearchAgentThe 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 fringe in the order provided by getSuccessors; you might get 244 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 (10 points) * 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 .5Does 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 (textbook section 3.2) without any changes.

python eightpuzzle.py

`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.
* Question 3 (10 points) * 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):
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 (10 points) * 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=manhattanHeuristicYou 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?
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.

* Question 5 (10 points) * 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=CornersProblemTo receive full credit, you need to define an abstract state representation that

`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 (10 points) * Implement a non-trivial, consistent heuristic for the

`CornersProblem`

in `cornersHeuristic`

.
Grading: inconsistent heuristics will get 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:* In class, we focused on tree
search, but there are some subtleties in applying A* to graph search. (Read
pp. 94-95 of the text carefully.) 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 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. Morevoer, if UCS and A* ever return paths of different lengths, your heuristic is inconsistent.

*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).

Additionally, any heuristic should always be non-negative, and should return a value of 0 at every goal state (technically this is a requirement for admissibility!). We will deduct 3 points for any heuristic that returns negative values, or doesn't behave properly at goal states.

* Question 7 (10 points)* Prove that consistency
implies admissibility

`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 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!)
If you have written your general 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 4902 search nodes.

* Question 8 (10 points) * Fill in

`foodHeuristic`

in `searchAgents.py`

with a `FoodSearchProblem`

. Try your agent on the `trickySearch`

board:
python pacman.py -l trickySearch -p AStarFoodSearchAgentOur UCS agent finds the optimal solution in about 13 seconds, exploring over 16,000 nodes. Any non-trivial consistent heuristic will receive 2 points. You will also receive the following additional points, depending on how few nodes your heuristic expands.

Fewer nodes than: | Points |
---|---|

Inf | 2 |

15000 | 4 |

12000 | 6 |

9000 | 8 (medium) |

7000 | 10 (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.

We will deduct 1 point for any heuristic that returns negative values, or does not return 0 at every goal state.

* Question 9 (5 points) *
Show that an inadmissable heuristic can lead to A* returning a suboptimal example, by giving an example search tree and heuristic where this occurs. Aim for as small a tree as possible, and explain your example clearly.

* Question 10 (10 points)* Give a detailed
example (by drawing the states and labeling the edges with costs)
where negative edge costs could lead to the following parodoxical
result: a finite state space with an optimal solution that does not reach
the goal in finite time. Be sure to explain why your example has this
property.

* Question 11 (5 points)*
Suppose you are asked to apply search to a symbol problem. You are told that
the solution is a sequence of at most five symbols, but the symbols are drawn
from an alphabet which contains thousands of symbols.
Formulate this problem as a search problem. If depth-first and breadth-first were your own
choices, which would you apply? Explain. How might you change the algorithm?

* Question 12 (5 points)*
Modify iterative deepening search so that it finds the lowest cost, as opposed to shortest, path.
What information do you need? Prove that your algorithm finds the optimal solution.

Here's a glossary of the key objects in the code base related to search problems, for your reference:

`SearchProblem (search.py)`

- A SearchProblem is an abstract object that represents the state space, successor function, costs, and goal state of a problem. You will interact with any SearchProblem only through the methods defined at the top of
`search.py`

`PositionSearchProblem (searchAgents.py)`

- A specific type of SearchProblem that you will be working with --- it corresponds to searching for a single pellet in a maze.
`CornersProblem (searchAgents.py)`

- A specific type of SearchProblem that you will define --- it corresponds to searching for a path through all four corners of a maze.
`FoodSearchProblem (searchAgents.py)`

- A specific type of SearchProblem that you will be working with --- it corresponds to searching for a way to eat all the pellets in a maze.
- Search Function
- A search function is a function which takes an instance of SearchProblem as a parameter, runs some algorithm, and returns a sequence of actions that lead to a goal. Example of search functions are
`depthFirstSearch`

and`breadthFirstSearch`

, which you have to write. You are provided`tinyMazeSearch`

which is a very bad search function that only works correctly on`tinyMaze`

`SearchAgent`

`SearchAgent`

is a class which implements an Agent (an object that interacts with the world) and does its planning through a search function. The`SearchAgent`

first uses the search function provided to make a plan of actions to take to reach the goal state, and then executes the actions one at a time.

Many thanks to Dan Klein for sharing his efforts to develop the pacman environment for AI instruction, and to Ron Parr for creating much of this assignment.