代写Pacman留学生、代做Python语言、代写SearchAgent、Python程序设计调试

日期：2019-05-22 11:03

Files you might want to look at:

pacman.py

game.py

util.py

4 Welcome to Pacman

After downloading the code (search.zip), 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:

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.

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 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 .

5 Question 1: Finding a Fixed Food Dot using Depth First Search

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 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:

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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. 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 util.py ! 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, 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).

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.

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 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.

6 Question 2: Breadth First Search

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.

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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 without any changes.

python eightpuzzle .py

7 Question 3: 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

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).

8 Question 4: A* search

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 ).

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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?

9 Question 5: 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.

Note: Make sure to complete Question 2 before working on Question 5, because Question 5 builds

upon your answer for Question 2.

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 1 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 Pacman 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.

10 Question 6: Corners Problem: Heuristic

Note: Make sure to complete Question 4 before working on Question 6, because Question 6 builds

upon your answer for Question 4.

Implement a non-trivial, consistent heuristic for the CornersProblem in cornersHeuristic .

python pacman.py l mediumCorners p AStarCornersAgent z 0.5

Note: AStarCornersAgent is a shortcut for

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p SearchAgent a fn=aStarSearch ,prob=CornersProblem , heuristic = cornersHeuristic

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!

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).

Grading: Your heuristic must be a non-trivial non-negative consistent heuristic to be accepted.

Make sure that your heuristic returns 0 at every goal state and never returns a negative value. Your

solution will be accepted if the number of nodes you will expand is at most 1600. Describe your

heuristic in a comment on top of the cornersHeuristic method.

11 Question 7: Eating All The Dots

Now 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 .

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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 5057

search nodes.

Note: Make sure to complete Question 4 before working on Question 7, because Question 7 builds

upon your answer for Question 4.

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 non-negative consistent heuristic will be accepted. Make sure that your heuristic

returns 0 at every goal state and never returns a negative value.

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.

Describe your heuristic in a comment on top of the foodHeuristic method.

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