Due Date: Friday, Feb 17th

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.

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. All the code and supporting files are on blackboard and the course webpage. For faster download, you can download the project folder here.

File Structure

Files you'll edit:

Algorithms.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:

search.py: Where the call for all search algorithms resides.

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 Algorithms.py and searchAgents.py during the assignment. You should start by downloading the folder for project1. You should submit Algorithms.py and searchAgents.py files with your code and comments to blackboard. Please do not change the other files in this distribution or submit any of our original files other than these files. If you work with a partner, please write their name in a file named partner.txt

Evaluation:

The project is out of 50. Each question is worth 10. Last question requires lots of your understanding of the problem and logical thinking! So, it has extra points. You could get 52/50 if you did well (and earned extra credit) in Question 5. You will be auto-grading your code for technical correctness (using autograder.py that you have used for Project 0).

Please do not change the names of any provided functions or classes within the code, or you will wreak havoc on the autograder. 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. You may find some questions in the writeup. You are not required to provide answers for them. These are just to get you thinking about your algorithm.

Academic Dishonesty:

We will be doing random checks of your code against other submissions in the class for logical redundancy. If you copy someone else's code and submit it with minor changes (and your code, get checked), we will know. We trust you all to submit your own work only; please don't let us down. Also, don’t give your code to other than your partner. If you do and you got detected, you will get a 0 for the project.

Getting Help:

You are not alone! If you find yourself stuck on something, contact the course staff 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 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.

Autograder:

To run autograder for all questions

python autograder.py

To run it for a specific question

python autograder.py -q q1

If your implementation is not successful, you are probably to get a zero. Autograder tells you what the test was, what you should return, and what you returned. So be sure to read that carefully. Of course, that does not mean you should not seek help, you are welcome to ask us anytime. This is meant just to tell you what happened in your algorithm.

Welcome to Pacman

After changing to the project1/ directory, you should be able to play a game of Pacman by typing the following 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.

Note: Above command is for working from the command line. For those using other environments such as Spyder in Anaconda, you may run the above (and all commands in the project) by replacing python with run in Spyder.

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

Ops. What happened?

If pacman gets stuck, you can exit the game by typing CTRL-c into your terminal (or other ways in other environments).

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/OS X, you can even run all these commands in order with bash commands.txt.

Finding a Fixed Food Dot using Search Algorithms

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 need to refer to Appendix A for 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=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 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). Again printing is very important to understand your options.

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. More hints on the implementation (i.e., an implementation procedure) are given in the code. 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 Algorithms.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 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 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 Algorithms.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 (textbook section 3.2) without any changes.

python eightpuzzle.py

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.

Question 3 (10 points)

Implement the uniform-cost graph search algorithm in the uniformCostSearch function in Algorithms.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).

Note: The cost functions are based on the horizontal position of the agent, NOT the contents of the maze.

A* search

Question 4 (10 points)

Implement A* graph search in the empty function aStarSearch in Algorithms.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?

Eating All The Dots

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.

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 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.

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 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! If you need help, don't hesitate to ask the course staff.

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 1 point for any heuristic that returns negative values, or doesn't behave properly at goal states.

Question 5 (10 points with additional 2 extra marks (bonus in the project) )

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 consistent heuristic will receive 4 points. You will also receive the following points, depending on how few nodes your heuristic expands.

Fewer nodes than Points

15000 4

12000 6

9000 8 (medium)

7000 10 (hard…extra credit)

Note that the last case will give you 2 extra credits.

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.

Note: it is okay here that your grading will take time for Question 5. Our solution took around 20 seconds and explored only 1818 nodes

Appendix A: Object Glossary

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.