代写COMP3411、代做java编程语言

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COMP3411/9814 Artificial Intelligence
Term 1, 2025
Assignment 1 – Search, Pruning and Treasure Hunting
Due: Friday 21 March, 10pm
Marks: 25% of final assessment
In this assignment you will be examining search strategies for the 15-puzzle,
and pruning in alpha-beta search trees. You will also implement an AI strat egy for an agent to play a text-based adventure game. You should provide
answers for Questions 1 to 3 (Part A) in a written report, and implement
your agent to interact with the provided game engine (Part B).
Note: Parts A and B must be submitted separately ! Submission details
are at the end of this specification.
Part A: Search Strategies and Alpha-Beta Pruning
Question 1: Search Strategies for the 15-Puzzle (2 marks)
For this question you will construct a table showing the number of states
expanded when the 15-puzzle is solved, from various starting positions, using
four different search strategies:
(i) Breadth First Search
(ii) Iterative Deepening Search
(iii) Greedy Search (using the Manhattan Distance heuristic)
(iv) A* Search (using the Manhattan Distance heuristic)
Download the file path search.zip from this directory:
https://www.cse.unsw.edu.au/~cs3411/25T1/code/
(or download it from here).
Unzip the file and change directory to path search:
unzip path_search.zip
cd path_search
Run the code by typing:
python3 search.py --start 2634-5178-AB0C-9DEF --s bfs
The --start argument specifies the starting position, which in this case is:
2 6 3 4
5 1 7 8
A B C
9 D E F
Start State
1 2 3 4
5 6 7 8
9 A B C
D E F
Goal State
The Goal State is shown on the right. The --s argument specifies the search
strategy (bfs for Breadth First Search).
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The code should print out the number of expanded nodes (by thousands)
as it searches. It should then print a path from the Start State to the Goal
State, followed by the number of nodes Generated and Expanded, and the
Length and Cost of the path (which are both equal to 12 in this case).
(a) Draw up a table in this format:
Start State BFS IDS Greedy A*
start1
start2
start3
Run each of the four search strategies from three specified starting posi tions, using the following combinations of command-line arguments:
Starting Positions:
start1: --start 1237-5A46-09B8-DEFC
start2: --start 134B-5287-960C-DEAF
start3: --start 7203-16B4-5AC8-9DEF
Search Strategies:
BFS: --s bfs
IDS: --s dfs --id
Greedy: --s greedy
A*S earch: --s astar
In each case, record in your table the number of nodes Expanded during
the search.
(b) Briefly discuss the efficiency of these four search strategies.
Question 2: Heuristic Path Search for 15-Puzzle (3 marks)
In this question you will be exploring a search strategy known as Heuristic
Path Search, which is a best-first search using the objective function:
fw(n) = (2 − w)g(n) + wh(n),
where h() is an admissible heuristic and w is a number between 0 and 2.
Heuristic Path Search is equivalent to Uniform Cost Search when w = 0,
to A* Search when w = 1, and Greedy Search when w = 2. It is Complete
for all w between 0 and 2.
(a) Prove that Heuristic Path Search is optimal when 0 ≤ w ≤ 1.
Hint: show that minimizing f(n) = (2 − w)g(n) + wh(n) is the same
as minimizing f
′
(n) = g(n) + h
′
(n) for some function h
′
(n) with the
property that h
′
(n) ≤ h(n) for all n.
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(b) Draw up a table in this format (the top row has been filled in for you):
start4 start5 start6
IDA* Search 48 1606468 52 3534563 54 76653772
HPS, w = 1.1
HPS, w = 1.2
HPS, w = 1.3
HPS, w = 1.4
Run the code on each of the three start states shown below, using
Heuristic Path Search with w = 1.1, 1.2, 1.3 and 1.4 .
Starting Positions:
start4: --start 8192-6DA4-0C5E-B3F7
start5: --start 297F-DEB4-A601-C385
start6: --start F5B6-C170-E892-DA34
Search Strategies:
HPS, w = 1.1: --s heuristic --w 1.1
HPS, w = 1.2: --s heuristic --w 1.2
HPS, w = 1.3: --s heuristic --w 1.3
HPS, w = 1.4: --s heuristic --w 1.4
In each case, record in your table the length of the path that was found,
and the number of nodes Expanded during the search. Include the com pleted table in your report.
(c) Briefly discuss the tradeoff between speed and quality of solution for
Heuristic Path Search with different values of w.
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Question 3: Game Trees and Pruning (4 marks)
(a) The following game tree is designed so that alpha-beta search will prune
as many nodes as possible. At each node of the tree, all the leaves in the
left subtree are preferable to all the leaves in the right subtree (for the
player whose turn it is to move).
MAX
MIN
MAX
MIN
10 11 8 9 1314 12 2 3 0 1 6 7 4 5 15
Trace through the alpha-beta search algorithm on this tree, showing the
values of alpha and beta at each node as the algorithm progresses, and
clearly indicate which of the original 16 leaves are evaluated (i.e. not
pruned).
(b) Now consider another game tree of depth 4, but where each internal node
has exactly three children. Assume that the leaves have been assigned
in such a way that alpha-beta search prunes as many nodes as possible.
Draw the shape of the pruned tree. How many of the original 81 leaves
will be evaluated?
Hint: If you look closely at the pruned tree from part (a) you will see
a pattern. Some nodes explore all of their children; other nodes explore
only their leftmost child and prune the other children. The path down
the extreme left side of the tree is called the line of best play or Principal
Variation (PV). Nodes along this path are called PV-nodes. PV-nodes
explore all of their children. If we follow a path starting from a PV-node
but proceeding through non-PV nodes, we see an alternation between
nodes which explore all of their children, and those which explore only
one child. By reproducing this pattern for the tree in part (b), you should
be able to draw the shape of the pruned tree (without actually assigning
values to the leaves or tracing through the alpha-beta search algorithm).
(c) What is the time complexity of alpha-beta search, if the best move is
always examined first (at every branch of the tree)? Explain why.
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Part B: Treasure Hunt (16 marks)
For this part you will be implementing an agent to play a simple text-based
adventure game. The agent is considered to be stranded on a small group of
islands, with a few trees and the ruins of some ancient buildings. The agent
is required to move around a rectangular environment, collecting tools and
avoiding (or removing) obstacles along the way.
The obstacles and tools within the environment are represented as follows:
Obstacles Tools
T tree a axe
- door k key
* wall d dynamite
˜ water $ treasure
The agent will be represented by one of the characters ^, v, < or >,
depending on which direction it is pointing. The agent is capable of the
following instructions:
L turn left
R turn right
F (try to) move forward
U (try to) unlock a door, using an key
C (try to) chop down a tree, using an axe
B (try to) blast a wall, tree or door, using dynamite
When it executes an L or R instruction, the agent remains in the same
location and only its direction changes. When it executes an F instruction,
the agent attempts to move a single step in whichever direction it is pointing.
The F instruction will fail (have no effect) if there is a wall or tree directly
in front of the agent.
When the agent moves to a location occupied by a tool, it automatically
picks up the tool. The agent may use a C, U or B instruction to remove an
obstacle immediately in front of it, if it is carrying the appropriate tool. A
tree may be removed with a C (chop) instruction, if an axe is held. A door
may be removed with a U (unlock) instruction, if a key is held. A wall, tree
or door may be removed with a B (blast) instruction, if dynamite is held.
Whenever a tree is chopped, the tree automatically becomes a raft which the
agent can use as a tool to move across the water. If the agent is not holding a
raft and moves forward into the water, it will drown. If the agent is holding a
raft, it can safely move forward into the water, and continue to move around
on the water, using the raft. When the agent steps back onto the land, the
raft it was using will sink and cannot be used again. The agent will need to
chop down another tree in order to get a new raft.
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If the agent attempts to move off the edge of the environment, it dies.
To win the game, the agent must pick up the treasure and then return to its
initial location.
Running as a Single Process
Download the file src.zip from this directory:
https://www.cse.unsw.edu.au/~cs3411/25T1/hw1raft
(or download it from here).
Copy the archive into your own filespace, unzip it, then type
cd src
javac *.java
java Raft -i s0.in
You should then see something like this:
~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~
~~ d * T a ~~
~~ *-* *** ~~
~~**** v ****~~
~~TTT** **TTT~~
~~ $ ** k ** ~~
~~ ** ** ~~
~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~
Enter Action(s):
This allows you to play the role of the agent by typing commands at the
keyboard (followed by ). Note:
• a key can be used to open any door; once a door is opened, it has effec tively been removed from the environment and can never be “closed”
again.
• an axe or key can be used multiple times, but each dynamite can be
used only once.
• C, U or B instructions will fail (have no effect) if the appropriate tool
is not held, or if the location immediately in front of the agent does
not contain an appropriate obstacle.
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Running in Network Mode
Follow these instructions to see how the game runs in network mode:
1. open two windows, and cd to the src directory in both of them.
2. choose a port number between 1025 and 65535 – let’s suppose you
choose 31415.
3. type this in one window:
java Raft -p 31415 -i s0.in
4. type this in the other window:
java Agent -p 31415
In network mode, the agent runs as a separate process and communicates
with the game engine through a TCPIP socket. Notice that the agent cannot
see the whole environment, but only a 5-by-5 “window” around its current
location, appropriately rotated. From the agent’s point of view, locations off
the edge of the environment appear as a dot.
We have also provided a C version of the agent, which you can run by typing
make
./agent -p 31415
Writing an Agent
At each time step, the environment will send a series of 24 characters to the
agent, constituting a scan of the 5-by-5 window it is currently seeing; the
agent must send back a single character to indicate the action it has chosen.
You are free to write the agent in any language of your choosing.
• If you are coding in Java, your main file should be called Agent.java
(you are free to use the supplied file Agent.java as a starting point)
• If you are coding in Python, your main file should be called agent.py
(you are free to use the supplied file agent.py as a starting point) and
the first line should specify the version of Python you are using, e.g.
#!/usr/bin/python3
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• If you are coding in C, you are free to use the files agent.c, pipe.c
and pipe.h as a starting point. You must include a Makefile with
your submission which, when invoked with the command “make”, will
produce an executable called agent.
• In other languages, you will have to write the socket code for yourself.
You may assume that the specified environment is no larger than 80 by 80,
but the agent can begin anywhere inside it.
Additional examples of input environments can be found in the directory
https://www.cse.unsw.edu.au/~cs3411/25T1/hw1raft/sample
(or download it from here).
Question
At the top of your code, in a block of comments, you must provide a brief
answer (one or two paragraphs) to this Question:
Briefly describe how your program works, including any algo rithms and data structures employed, and explain any design de cisions you made along the way.
Submission
Parts A and B should be submitted separately.
You should submit your report for Part A by typing
give cs3411 hw1a hw1a.pdf
You should submit your code for Part B by typing
give cs3411 hw1raft ...
(Replace ... with the names of your submitted files)
You can submit as many times as you like – later submissions will overwrite
earlier ones. You can check that your submission has been received by using
one of this command:
3411 classrun -check
The submission deadline is Friday 21 March, 10 pm.
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A penalty of 5% will be applied to the mark for every 24 hours late after the
deadline, up to a maximum of 5 days (in accordance with UNSW policy).
Additional information may be found in the FAQ and will be considered as
part of the specification for the project. Questions relating to the project
can also be posted to the course Forums. If you have a question that has
not already been answered on the FAQ or the Forums, you can email it to
cs3411@cse.unsw.edu.au
Please ensure that you submit the source files and NOT any binary files. The
give system will compile your program using your Makefile and check that
it produces a binary file (or java class files) with the correct name.
Assessment
Your program will be tested on a series of sample inputs with successively
more challenging environments. There will be:
• 10 marks for functionality (automarking)
• 6 marks for Algorithms, Style, Comments and answer to the Question
You should always adhere to good coding practices and style. In general,
a program that attempts a substantial part of the job but does that part
correctly will receive more marks than one attempting to do the entire job
but with many errors.
Plagiarism Policy Group submissions will not be allowed. Your program
must be entirely your own work. Plagiarism detection software will be used
to compare all submissions pairwise (including submissions for similar assign ments in previous years, if applicable) and serious penalties will be applied,
including an entry on UNSW’s plagiarism register.
You are also not allowed to submit code obtained with the help of ChatGPT,
Claude, GitHub Copilot, Gemini or similar automatic tools.
• Do not copy code from others; do not allow anyone to see your code.
• Do not copy code from the Internet; do not develop or upload your own
code on a publicly accessible repository.
• Code generated by ChatGPT, Claude, GitHub Copilot, Gemini and
similar tools will be treated as plagiarism.
Please refer to the on-line resources to help you understand what plagiarism
is and how it is dealt with at UNSW:
• Academic Integrity and Plagiarism
• UNSW Plagiarism Policy
Good luck!
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