DM828, Assignment 1: Search in Pac-Man

All those colored walls,
Mazes give Pac-Man the blues,
So teach him to search.

Premise

The Pac-Man projects were developed for UC Berkeley's introductory artificial intelligence course, CS 188. They apply an array of AI techniques to playing Pac-Man. However, these projects don't focus on building AI for video games. Instead, they teach foundational AI concepts, such as informed state-space search, probabilistic inference, and reinforcement learning. These concepts underly real-world application areas such as natural language processing, computer vision, and robotics.

Introduction

In this project, your Pac-Man 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 Pac-Man 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 tgz archive.

Files you'll edit:
`search.py`	Where all your search algorithms will reside.
`searchAgents.py`	Where all your search-based agents will reside.
Files you might want to look at:
`pacman.py`	The main file that runs Pac-Man games. This file describes a Pac-Man GameState type, which you use in this project.
`game.py`	The logic behind how the Pac-Man 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 Pac-Man
`graphicsUtils.py`	Support for Pac-Man graphics
`textDisplay.py`	ASCII graphics for Pac-Man
`ghostAgents.py`	Agents to control ghosts
`keyboardAgents.py`	Keyboard interfaces to control Pac-Man
`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). Submission is done via black board "Assignment Hand In".

Deadline:

23.59 of Monday, November 21, 2011

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 the autograder. However, the correctness of your implementation -- not the autograder's output -- will be the final judge of your score.

The grade serves only as a form of feedback and it will not be used for the final grade that depends only by the written exam. For this assignment: if you make an honest effort at some of the questions you will pass, otherwise fail. The accomplishment of the first 4 questions is necessary and sufficient condition to pass the assignment. The remaining questions are optional.

Getting Help: You are not alone! If you find yourself stuck on something, ask your colleagues or contact the teacher for help. He doesn't know when or how to help unless you ask. But in any case do not ask for code, ask only verbal help. You must implement the required procedures yourself. One more piece of advice: if you don't know what a variable does or what kind of values it takes, print it out.

Welcome to Pac-Man

After downloading the code (search.tgz), unzipping it and changing to the search directory, you should be able to play a game of Pac-Man by typing the following at the command line:

python pacman.py

Note: if you get error messages regarding python-tk, use your package manager to install python-tk, or see this page for more detailed instructions. Pac-Man lives in a shiny blue world of twisting corridors and tasty round treats. Navigating this world efficiently will be Pac-Man'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 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. Try it out now.

Finding a Fixed Food Dot using Search Algorithms

In searchAgents.py, you'll find a fully implemented SearchAgent, which plans out a path through Pac-Man'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=tinyMazeSearch

The command above tells the SearchAgent to use tinyMazeSearch as its search algorithm, which is implemented in search.py. Pac-Man should navigate the maze successfully.

Now it's time to write full-fledged generic search functions to help Pac-Man plan routes! Pseudocode and explanation for the search algorithms can be found in the 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.

Hint: If you panic and think you will not be able to meet the submission deadline, a file implementations.py with implementations of search algorithms is made available under "Documents" from the Black Board system. The name of each algorithm has been encrypted and you have to recognise which is which. It is very important that you understand what the algorithms are doing. If you use this file, import it (from implementations import *) in your search.py, comment out the templates of the search algorithms in that file and write at the end of the file the mapping of function names as shown by the Abbreviations. Each algorithm is very similar. Algorithms for DFS, BFS, UCS, and A* differ only in the details of how the frontier is managed. Indeed in the implementation there is only a single generic search method which is then configured with an algorithm-specific queuing strategy.

Hint: Make sure to check out the Stack, Queue and PriorityQueue types provided to you in util.py!

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).

Question 1 Implement or recognize the tree search, the graph search and the depth-first search (DFS) algorithms and link these functions in search.py with names treeSearch, graphSearch and depthFirstSearch. There are two versions of DFS depending whether they do graph search or tree search. You have to link the one that guarantees your algorithm to be complete.

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 -p SearchAgent -z .5

(The -z flag allows us to indicate a rescaling factor.) The Pac-Man 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 Pac-Man actually go to all the explored squares on his way to the goal?

Hint: If you chose or implemented right your DFS, the solution found by your 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 Implement or recognize the breadth-first search (BFS) algorithm dubbed breadthFirstSearch function in search.py. Again, choose right between tree and graph search. 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 Pac-Man 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 Pac-Man 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 Pac-Man agent should adjust its behavior in response.

Question 3 Implement or recognize the uniform-cost graph search algorithm in the uniformCostSearch function in search.py. You may look through util.py for the data structure to use 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).

A* search

Question 4 Implement A* search or recognize it from implementation.py and dub it 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?

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.

Question 5 (2 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=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 Pac-Man 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 Pac-Man 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) Implement a heuristic for the CornersProblem in cornersHeuristic. Grading: inadmissible heuristics will get no credit. 1 point for any admissible heuristic. 1 point for expanding fewer than 1600 nodes. 1 point for expanding fewer than 1200 nodes. Expand fewer than 800, and you're doing great!

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.

Eating All The Dots

Now we'll solve a hard search problem: eating all the Pac-Man 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 Pac-Man 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 Pac-Man. (Of course ghosts can ruin the execution of a solution but we do not consider this here.) 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 (5 points) 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. 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. If your heuristic is not only admissible, but also consistent, you will receive 1 additional point for this question.

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

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 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. (If you don't understand why, ask a GSI!) 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 Pac-Man agents.

python pacman.py -l bigSearch -p ApproximateSearchAgent -z .5 -q

We 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.

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 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.