In this project, you’ll implement an agent that can diagnose monster diseases. Based on a list of
diseases and their ailments and a list of elevated and reduced vitamin levels, you will diagnosis the
disease(s) affecting a particular monster. You will submit the code for diagnosing these monsters to the
Mini-Project 5 assignment in Gradescope. You will also submit a report describing your agent to Canvas.
Your grade will be based on a combination of your report (50%) and your agent’s performance (50%).
About the Project
Monster physiology relies on a balance of 26 vitamins, conveniently named Vitamin A through Vitamin Z.
Different monster ailments can cause elevated or reduced levels for different vitamins. Unfortunately,
every ailment affects every monster species differently, so there is no canonical list of all monster
ailments and their effects; instead, the ailments must be interpreted in the context of a particular species.
In this project, you’ll diagnose a particular monster based on its symptoms and a list of ailments and their
effects on that monster species. Both the symptoms of an ailment and a monster’s symptoms will be
represented by a dictionary, where the keys are the different vitamin letters and the values are either +
(for elevated), – (for reduced), or 0 (for normal). For example:
{“A”: “+”, “B”: “0”, “C”: “-“, “D”: “0”, “E”: 0, “F”: “+”, …}
This would represent elevated levels of Vitamins A and F, and a reduced level of Vitamin C. This could
be the symptoms of a particular patient (e.g. “Sully presented with elevated levels of Vitamins A and F,
and a reduced level of Vitamin C), or it could be the symptoms of a particular disease (e.g. “Alphaitis
causes elevated Vitamins A and F and a reduced Vitamin C in monsters like Sully”).
Your Agent
To write your agent, download the starter code below. Complete the solve() method, then upload it to
Gradescope to test it against the autograder. Before the deadline, make sure to select your best
performance in Gradescope as your submission to be graded.
Starter Code
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Here is your starter code: MonsterDiagnosisAgent.zip
(https://gatech.instructure.com/courses/453236/files/62351111/download) .
The starter code contains two files: MonsterDiagnosisAgent.py and main.py. You will write your agent in
MonsterDiagnosisAgent.py. You may test your agent by running main.py. You will only submit
MonsterDiagnosisAgent.py; you may modify main.py to test your agent with different inputs.
Your solve() method will have two parameters. The first will be a list of diseases and their symptoms.
This will be provided as a dictionary, where the keys are the names of diseases and their values are
each a dictionary representing its symptoms. For example:
{“Alphaitis”: {“A”: “+”, “B”: “0”, “C”: “-“, “D”: “0”, “E”: “0”, “F”: “+”, …},
“Betatosis”: {“A”: “0”, “B”: “+”, “C”: “-“, “D”: “0”, “E”: “+”, “F”: “-“, …},
“Gammanoma”: {“A”: “0”, “B”: “0”, “C”: “+”, “D”: “+”, “E”: “+”, “F”: “+”, …}, …]
There may be up to 24 diseases. Each disease will have values for all 26 vitamins. Most vitamins will be
unaffected by any particular disease; most diseases only affect 3-6 vitamins.
The second parameter to the function will be a particular set of symptoms, given as a dictionary, such
as:
{“A”: “+”, “B”: “0”, “C”: “-“, “D”: “0”, “E”: 0, “F”: “+”, …}
Your goal is to identify the smallest subset of diseases from the list of ailments that can explain the
monster’s symptoms.
If the patient has two diseases with opposite effects, they cancel each other out. For example, if a
patient had both Alphaitis and Betatosis (according to the definitions above), they would have a normal
level of Vitamin F because Alphaitis elevates F and Betatosis reduces F.
If the patient has two diseases with the same effect, their effect remains the same. For example, if a
patient had both Alphaitis and Betatosis (according to the definitions above), they would have a reduced
level of Vitamin C because both diseases reduce Vitamin C. There is no extra effect from having multiple
diseases with the same effect.
If a patient has more than two diseases, then each Vitamin moves in whichever direction is caused by
the largest number of diseases. For example, if a patient had Alphaitis, Betatosis, and Gammanoma,
they would exhibit reduced levels of Vitamin C: both Alphaitis and Betatosis reduce Vitamin C, while
Gammanoma elevates it. Two reductions plus one elevation leads to a reduction. If, on the other hand,
they had four diseases, two of which reduced Vitamin C and two of which elevated Vitamin C, their
Vitamin C levels would be normal.
Returning Your Solution
Your solve() method should return a list of strings. Each string should be the name of one of the (up to)
24 diseases. Together, the diseases should explain all of the symptoms of the patient. If there are
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multiple sets of diseases that can explain all the symptoms, then you should return the set with the
minimum number of diseases according to the principle of parsimony. For this project, you may assume
that all diseases are equally likely and that all symptoms will be covered.
For the two test cases in the starter code, the answers should be: [“Alphaitis”, “Betatosis”] and
[“Gammanoma”, “Deltaccol”, “Epsicusus”].
Submitting Your Solution
To submit your agent, go to the course in Canvas and click Gradescope on the left side. Then, select
CS7637 if need be.
You will see an assignment named Mini-Project 5. Select this project, then drag your
MonsterDiagnosisAgent.py file into the autograder. If you have multiple files, add them to a zip file and
drag that zip file into the autograder.
When your submission is done running, you’ll see your results.
How You Will Be Graded
Your agent will run against 20 test cases. The first two of these will always be the same; these are those
contained in the original main.py. The last 18 will be randomly generated.
You can earn up to 40 points. You will earn one point for each of the test cases for which you correctly
identify a list of diseases that explain all the symptoms. You will earn an additional point for each of the
test cases for which you identify the smallest list of diseases that explain all the symptoms.
You may submit up to 40 times prior to the deadline. The large majority of students do not need nearly
that many submissions, so do not feel like you should use all 40; this cap is in place primarily to prevent
brute force methods for farming information about patterns in hidden test cases or submitting highly
random agents hoping for a lucky submission. Note that Gradescope has no way for us to increase your
individual number of submissions, so we cannot return submissions to you in the case of errors or other
issues, but you should have more than enough submissions to handle errors if they arise.
You must select which of your submissions you want to count for a grade prior to the deadline. Note that
by default, Gradescope marks your last submission as your submission to be graded. We cannot
automatically select your best submission. Your agent score is worth 50% of your overall mini-project
grade.
Your Report
In addition to submitting your agent to Gradescope, you should also write up a short report describing
your agent’s design and performance. Your report may be up to 4 pages, and should answer the
following questions:
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How does your agent work? Does it use some concepts covered in our course? Or some other
approach?
How well does your agent perform? Does it struggle on any particular cases?
How efficient is your agent? How does its performance change as the number of diseases grows?
Does your agent do anything particularly clever to try to arrive at an answer more efficiently?
How does your agent compare to a human? Do you feel people approach the problem similarly?
You are encouraged but not required to include visuals and diagrams in your four page report. The
primary goal of the report is to share with your classmates your approach, and to let you see your
classmates’ approaches. You may include code snippets if you think they are particularly novel, but
please do not include the entirety of your code.
Tip: Remember, we want to see how you put the content of this class into action when designing your
agent. You don’t need to use the principles and methods from the lectures precisely, but we want to
see your knowledge of the content reflected in your terminology and your reflection.
Submission Instructions
Complete your assignment using JDF format
(https://gatech.instructure.com/courses/453236/files/folder/Journal%20Templates#) , then save your
submission as a PDF. Assignments should be submitted via this Canvas page. You should submit a
single PDF for this assignment. This PDF will be ported over to Peer Feedback for peer review by your
classmates. If your assignment involves things (like videos, working prototypes, etc.) that cannot be
provided in PDF, you should provide them separately (through OneDrive, Google Drive, Dropbox, etc.)
and submit a PDF that links to or otherwise describes how to access that material.
After submitting, download your submission from Canvas to verify that you’ve uploaded the
correct file. Review that any included figures are legible at standard magnification, with text or symbols
inside figures at equal or greater size than figure captions.
This is an individual assignment. All work you submit should be your own. Make sure to cite any
sources you reference, and use quotes and in-line citations to mark any direct quotes.
Late work is not accepted without advance agreement except in cases of medical or family emergencies.
In the case of such an emergency, please contact the Dean of Students
(https://studentlife.gatech.edu/request-assistance) .
Grading Information
Your report is worth 50% of your mini-project grade. As such, your report will be graded on a 40-point
scale coinciding with a rubric designed to mirror the questions above. Make sure to answer those
questions; if any of the questions are irrelevant to the design of your agent, explain why.
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Mini-Project 5 Journal Rubric
Peer Review
After submission, your assignment will be ported to Peer Feedback (http://peerfeedback.gatech.edu/) for
review by your classmates. Grading is not the primary function of this peer review process; the primary
function is simply to give you the opportunity to read and comment on your classmates’ ideas, and
receive additional feedback on your own. All grades will come from the graders alone. See the
course participation policy (https://gatech.instructure.com/courses/453236/assignments/2084928) for full
details about how points are awarded for completing peer reviews.
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Criteria Ratings Pts
JDF Format
Does your submission
conform to the
important parts of JDF
formatting—that is,
margin size, line
spacing, font, and font
size? (Deduction only)
0 pts
Agent Description
15 points: How does
your agent work? Does
it use some concepts
covered in our course?
Or some other
approach?
15 pts
Agent Performance
10 points: How well
does your agent
perform? Does it
struggle on any
particular cases?
10 pts
0 pts
Correctly Formatted
Your essay conforms to the
important portions of JDF formatting.
0 pts
JDF Error [Deduction]
Your submission violates JDF format
in one or more substantive ways:
font size, typeface, margins, or line
spacing. See the comment for more
details. As a result, you have been
assigned a negative score on this
rubric item to deduct from your
assignment score.
15 pts
Full Credit
You have
adequately and
thoroughly
described your
agent’s
operations.
10 pts
2/3rds Credit
You have made
an attempt to
describe your
agent’s
operations, but
your description
is lacking a
couple critical
details, such as
adequate detail
on how it
implements the
method it uses.
See the
comment for
more details.
5 pts
1/3rd Credit
You have made
an attempt to
describe your
agent’s
operations, but
your description
is lacking
several
significant
details, such as
what strategy it
implements and
the details of
that strategy’s
implementation.
See the
comment for
more details.
0 pts
No Credit
Your journal
makes little to
no effort to
describe how
your agent
operates.
10 pts
Full Credit
You have adequately
described your agent’s
performance, including
how many problems it
gets right and what
kinds of problems (if
any) it struggles on.
5 pts
1/2 Credit
You have made an
attempt to describe
your agent’s
performance, but you
have left out significant
details, such as how
many test cases it gets
right or what it
struggles on and why.
0 pts
No Credit
Your journal makes
little to no attempt to
describe the
performance of your
agent in terms of the
number of problems it
solves and where it
struggles.
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Criteria Ratings Pts
Agent Efficiency
5 points: How efficient
is your agent? How
does its performance
change as the number
of diseases grows?
(Note: Using Big O
notation is
recommended, but not
required; it is just
easier to know you’ve
adequately answered
the prompt if you
include a Big O
analysis.)
5 pts
Human Comparison
10 points: How does
your agent compare to
a human? Does your
agent solve the
problem the same way
you would?
10 pts
Total Points: 40
See the comment for
more details.
5 pts
Full Credit
You have described
your agent’s efficiency,
in terms of both how
much time it takes at
present and in terms of
how the runtime
changes as the
number of diseases
grows.
2.5 pts
1/2 Credit
You have made some
attempt to describe the
efficiency of your
agent, but you have left
out one or more
important details, such
as how the runtime
changes as the
number of diseases
grows. See the
comment for more
details.
0 pts
No Credit
Your journal makes
little to no attempt to
describe the
performance of your
agent in terms of its
runtime efficiency.
10 pts
Full Credit
You have discussed in
adequate detail how
your agent compares
to a human, including
the similarities and
differences between
both its reasoning
strategy and its likely
performance.
5 pts
1/2 Credit
You have made some
attempt to compare
your agent to humans,
but your analysis is
lacking in one or more
significant areas. It
may be lacking
adequate depth, a
sufficient comparison
in terms of both
similarities and
differences, or a
sufficient comparison
of both performance
and strategy.
0 pts
No Credit
Your journal makes
little to no attempt to
describe how your
agent’s strategy and
performance compares
to that of a human.
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