In this project, you will design Pacman agents that use sensors to locate and eat invisible
ghosts. You’ll advance from locating single, stationary ghosts to hunting packs of multiple
moving ghosts with ruthless efficiency.
As in previous projects, this project includes an autograder for you to grade your answers
on your machine. This can be run with the command:
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. Download search.zip from here http://ai.berkeley.edu/tracking.html which
will contain all the code and supporting files.
1 Files to edit
For this project you will need to edit the following files
• bustersAgents.py: Agents for playing the Ghostbusters variant of Pacman.
• inference.py: Code for tracking ghosts over time using their sounds.
2 Ghostbusters and Bayes Nets (100 pts)
Your primary task in this project is to implement inference to track the ghosts. For the
keyboard based game above, a crude form of inference was implemented for you by default: all squares in which a ghost could possibly be are shaded by the color of the ghost.
Naturally, we want a better estimate of the ghost’s position. Fortunately, Bayes’ Nets provide us with powerful tools for making the most of the information we have. Throughout
the rest of this project, you will implement algorithms for performing both exact and approximate inference using Bayes’ Nets.
For all the problem titles described below, please refer to the link http://ai.berkeley.
edu/tracking.html for the problem description and what is expected of each problem.
As always autograder has different test cases against which you can run your program
to check the correctness. Please ensure your code is readable and use comments in your
code to make it more clear for the person reading your code.
You will be graded on your code implementation for the following questions:
1. Exact Inference Observation (12 pts)
2. Exact Inference with Time Elapse (12 pts)
3. Exact Inference Full Test (12 pts)
4. Approximate Inference Observation (12 pts)
5. Approximate Inference with Time Elapse (16 pts)
6. Joint Particle Filter Observation (16 pts)
7. Joint Particle Filter with Time Elapse (16 pts)
3 Self Analysis (5 pts)
1. What was the hardest part of the assignment for you?
2. What was the easiest part of the assignment for you?
3. What problem(s) helped further your understanding of the course material?
4. Did you feel any problems were tedious and not helpful to your understanding of
5. What other feedback do you have about this homework?
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. If your code passes all the test cases in the autograder you would receive full
points for the implementation.
However even if your code does not necessarily pass all the test cases, we would evaluate
your code and then award you partial points accordingly. In such cases it would be even
more beneficial if you could give a short description of what you tried and where you
had failed and that would help us in giving you better points.
5 Submission Instructions
• For the final submission you would be turning in a zipped folder of the python
files and a PDF document containing your responses to questions from previous
• Please ensure all the submissions are done through canvas. Please do not email the
instructor or the TA’s with your submission. Submissions made via email would not
be considered for grading. 2
• Naming: Your upload should be named in the format huidi-Projhnumberi.zip where
huidi is your Utah uid and hnumberi is the Project number. Ex: u0006300-Proj0.pdf
• For this project fill in portions of the files to edit. Once you have completed the code,
zip your entire project folder, rename it as per the conventions stated above and
submit it via canvas. Do not delete the other files present in the .zip file or change
the names of any of those files in the project directory.
• Written Answers: Place all your written answers and responses to questions in ”Self
Analysis” in a single PDF document. This should be clearly named in the format
huidi-Projhnumberi-answers.pdf, where huidi is your Utah uid and hnumberi is
the Project number. Ex: u0006300-Proj0-answers.pdf Please make sure to write your
name at the top of the document!
• Group Submissions: If you haven’t done this already for previous projects and if
you are working in groups, You need to sign up to one of the project groups in
the people page in canvas and under the groups tab. Please sign up to one of the
”project groups” and ensure that your group member is signed up to the same
group. This ensures that submissions from one of the group members counts for
the entire group. Follow the file naming convention for the group member uploading the submission. i.e. do not mention the uid of your partner on the file name but
mention your partner’s details inside your answers.pdf .
Note: Each group is limited to maximum 2 members, so if a group is taken already
please choose the next free group.