We also thank Daniel Urieli for the initial adaptation of this assignment for the CS343 Artificial Intelligence course at The University of Texas at Austin.

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.

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 (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 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 |

`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 (only) along with a `README.txt`

file.

This assignment should be submitted via `turnin`

with the assignment name `cs343-1-search`

using these submission instructions.

**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:** 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, section, and the newsgroup 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. One more piece of advice: if you don't know what a variable does or what kind of values it takes, print it out.

In order to expedite your development, the course staff has supplied an autograder which includes graph based test cases. We encourage you to master these test cases and debug on them before running your code with Pacman. Since the Pacman world is considerably more complex, and generally has much more state than the graph based test cases, debugging your code using Pacman will be a difficult and error prone process.

This page will show you some additional ways to invoke the autograder which you may find helpful during your development process. For example, to invoke the autograder for question 2 only, run

python autograder.py -q q2

Note that the extra credit is invoked using -q extra.

If you notice that you are failing a particular test within question 2, such as graph_infinite.test, you can specify that you would like the autograder to run only that test as follows

python autograder.py -t test_cases/q2/graph_infinite

Notice that the argument given is the actual path to the file specifying the test case itself, sans the .test extension. If you explore the test_cases directory, you will notice that there is a subdirectory corresponding to each question, and that there is a .solution file corresponding to each test.

Finally, if you would like the autograder to display both the test case and the solution for all tests it runs, you may add the flag -p as follows

python autograder.py -p -t test_cases/q2/graph_bfs_vs_dfs

Once you're passing the graph based test cases and have used those to debug your code, we encourage you to give Pacman a try and watch your code help him navigate his world

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 GoWestAgentBut, things get ugly for this agent when turning is required:

python pacman.py --layout tinyMaze --pacman GoWestAgentSoon, 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 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`

.
`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 (2 points) **

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 (textbook section 3.5).

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 (1 point) **

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 to 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 (2 points) **

Implement the uniform-cost graph search algorithm in
the `uniformCostSearch`

function in `search.py`

. 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 (3 points) **

Implement A* graph search in the empty function `aStarSearch`

in `search.py`

. A* takes a heuristic function as an argument. Heuristics take two argument: 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 (2 points) ***[Dependency: Q2]*

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 (3 points) ***[Dependency: Q4]*

Implement a heuristic for the `CornersProblem`

in `cornersHeuristic`

. Grading: inadmissible heuristics will get *no* credit.

- 1 point for any admissible heuristic expanding fewer than 2000 nodes.
- 2 points for expanding fewer than 1600 nodes.
- 3 points for expanding fewer than 1200 nodes.

python pacman.py -l mediumCorners -p AStarCornersAgent -z 0.5

*Hint:* Remember, heuristic functions just return numbers, which, to be admissible, must be lower bounds on the actual shortest path cost to the nearest goal.

*Note:* `AStarCornersAgent`

is a shortcut for `-p SearchAgent -a fn=aStarSearch,prob=CornersProblem,heuristic=cornersHeuristic`

.

`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 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 7 (4 points) ***[Dependency: Q4]*

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. If your heuristic is admissible, you will receive the following score, depending on how many nodes your heuristic expands.

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

15000 | 1 |

12000 | 2 |

9000 | 3 (medium) |

7000 | 4 (hard) |

If your heuristic is inadmissible, you will receive *no* credit, so be careful! Think through admissibility carefully, as inadmissible heuristics may manage to produce fast searches and even optimal paths. Can you solve `mediumSearch`

in a short time? If so, we're either very, very impressed, or your heuristic is inadmissible.

*Admissibility vs. Consistency?*Technically, admissibility isn't enough to guarantee correctness in graph search -- you need the stronger condition of consistency. For a heuristic to be consistent, it must hold that if an action has cost *c*, then taking that action can only cause a drop in heuristic of at most *c*.

Almost always, admissible heuristics are also consistent, especially if they are derived from problem relaxations. Therefore it is probably 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. Inconsistency can sometimes be detected by verifying that your returned solutions are non-decreasing in f-value. Morevoer, if UCS and A* ever return paths of different lengths, your heuristic is inconsistent (and inadmissible, too). This stuff is tricky. If you need help, don't hesitate to ask the course staff!

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 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.

* Question 8 (2 points)* 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. In fact, you can do better if you try.

* Mini Contest (2 points extra credit)* Implement an

`ApproximateSearchAgent`

in `searchAgents.py`

that finds a short path through the `bigSearch`

layout. The three teams that find the shortest path using no more than 30 seconds of computation will receive 2 extra credit points and an in-class demonstration of their brilliant Pacman agents.
python pacman.py -l bigSearch -p ApproximateSearchAgent -z .5 -qWe will time your agent using the no graphics option

`-q`

, and it must complete in under 30 seconds on our grading machines. Please describe what your agent is doing in a comment! We reserve the right to give additional extra credit to creative solutions, even if they don't work that well. Don't hard-code the path, of course.
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 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.