In this project, you’ll implement an agent that will learn a definition of a particular monster species from a
list of positive and negative samples, and then make a determination about whether a newly-provided
sample is an instance of that monster species or not. You will submit the code for identifying these
monsters to the Mini-Project 4 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
For the purposes of this project, every monster has a value for each of twelve parameters. The possible
values are all known. The parameters and their possible values are:
size: tiny, small, medium, large, huge
color: black, white, brown, gray, red, yellow, blue, green, orange, purple
covering: fur, feathers, scales, skin
foot-type: paw, hoof, talon, foot, none
leg-count: 0, 1, 2, 3, 4, 5, 6, 7, 8
arm-count: 0, 1, 2, 3, 4, 5, 6, 7, 8
eye-count: 0, 1, 2, 3, 4, 5, 6, 7, 8
horn-count: 0, 1, 2
lays-eggs: true, false
has-wings: true, false
has-gills: true, false
has-tail: true, false
A single monster will be defined as a dictionary with those 12 keys. Each value will be one of the values
from the corresponding list. The values associated with size, color, covering, and foot-type will be
strings; with leg-count, arm-count, eye-count, and horn-count will be integers; and with lays-eggs, haswings, has-gills, and has-tail will be booleans.
You will be given a list of monsters in the form of a list of dictionaries, each of which has those twelve
keys and one of the listed values. Each monster will be labeled as either True (an instance of the
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species of monster we are currently looking at) or False (not an instance of the species of monster we
are currently looking at). You will also be given a single unlabeled monster; your goal is to return a
prediction—True or False—of whether the unlabeled monster is an instance of the species of monster
defined by the labeled list.
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
Here is your starter code: MonsterClassificationAgent.zip
(https://gatech.instructure.com/courses/453236/files/62351089/download) .
The starter code contains two files: MonsterClassificationAgent.py and main.py. You will write your agent
in MonsterClassificationAgent.py. You may test your agent by running main.py. You will only submit
MonsterClassificationAgent.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 2-tuples. The first item in each
2-tuple will be a dictionary representing a single monster. The second item in each 2-tuple will be a
boolean representing whether that particular monster is an example of this new monster species. The
second parameter to solve() will be a dictionary representing the unlabeled monster.
Each monster species might have multiple possible values for each of the above parameters. One
monster species, for instance, include monsters with either 1 or 2 horns, but never 0. Another species
might include monsters that can be red, blue, and yellow, but no other colors. Another species might
include both monsters with and without wings. So, while each monster is defined by a single value for
each parameter, the species as a whole may have more variation.
Returning Your Solution
Your solve() method should return True or False based on whether your function believes this new
monster (the second parameter) to be an example of the species defined by the labeled list of monsters
(the first parameters).
Not every list will be fully exhaustive. Your second parameter could, for example, feature a monster that
is a color that never appeared as positive or negative in the list of samples. Your agent’s task is to make
an educated guess. For example, you might determine, “The only difference between this monster and
the positive examples is its color, and its color never appeared in the negative examples, therefore there
is a good likelihood that this is still a positive example.”
You may assume that the parameters are independent; for example, you will not have any species that
has one horn when yellow and two horns when blue, but never one horn when blue. You may assume
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that all parameters are equally likely to occur; for example, you will not have any species that is yellow
90% of the time and blue only 10% of the time. Those ratios may appear in the list of samples you
receive, but the underlying distribution of possibilities will be even. You may assume that these
parameters are all that there is: if two monsters have the exact same parameters, they are guaranteed to
be the same species. Finally, you should assume that each list is independent: you should not use
knowledge from a prior test case to inform the current one.
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 4. Select this project, then drag your
MonsterClassificationAgent.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 four of these will always be the same; these are those
contained in the original main.py. The last 16 will be randomly generated.
You can earn up to 40 points. Because the list of labeled monsters is non-exhaustive, it is highly unlikely
you can write an agent that classifies every single monster correctly; there will always be some
uncertainty. For that reason, you will receive full credit if your agent correctly classifies 17 or more of the
monsters. Similarly, because every label is a simple true/false, even a randomly performing agent can
likely get 50% correct with no intelligence under the hood. For that reason, you will receive no credit if
your agent correctly classifies 7 or fewer monsters.
Between 7 and 17, you will receive 4 points for each correct classification: 4 points for 8/20, 8 for 9/20;
12 for 10/20; and so on, up to 40 points for correctly classifying 17 out of 20 or better.
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.
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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:
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 labeled monsters
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) .
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Mini-Project 4 Journal Rubric
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.
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 labeled monsters
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 labeled
monsters 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 labeled
monsters 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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