The Ninth Symposium on Educational Advances in Artificial Intelligence, Honolulu, Hawaii, USA (collocated with
Recommender system using MapReduce
||Students gain hands-on experience with
recommender systems, modeling the core computation in a
spreadsheet, and then expressing the recommender algorithm in the
MapReduce paradigm using mrjob with the Movielens data set.
Building a Fake News Detector
Michael Guerzhoy and Lisa
||Students use Machine Learning to classify news
headlines as "real" or "fake". Students build and compare several
standard classifiers. A classwide competition is held for the best
fake news detector.
|Using Ultimate Tic
Tac Toe to Motivate AI Game Agents
||In this assignment students extend a Tic Tac
Toe program to Ultimate Tic Tac Toe and implement a different
search strategy than the example code.
|RISK AI Project
||Students design and implement agents for the
board game of RISK which then compete in a class tournament for
extra credit. Two preliminary assignments are also included, where
students implement uniform cost search and decision trees within
the context of RISK.
|The Minecraft Projects
Adam Summerville and Joseph
||A package of interesting projects in
Minecraft-like domains with applications to Minecraft itself.
These assignments cover a variety of AI topics from path and task
planning to constraint satisfaction.
Depth First Learning: DeepStack
Surya Bhupatiraju and Kumar
||Through six sessions,
students will gain understanding of how to solve 1v1 games of
incomplete information by traversing the tree of concept
dependencies that led to DeepStack, the successful AI poker
Nearest Neighbor Classification
(with almost no background)
Nate Derbinsky and
||This assignment engages
students in basic Machine Learning concepts and implementation,
including classification and similarity-based search, with minimal
|Introduction to Python
for Data Science
Marion Neumann and
||This is a sequence of two interactive guided
labs introducing the basics of Python the data science workflow
using the Iris dataset.
|Introducing the Data
Science Workflow using Sentiment Analysis
Marion Neumann and
||This is an interactive guided lab introducing
the basic data science workflow by exploring sentiment analysis.
The assignment highlights data acquisition and exploration using a
given dataset of movie reviews.
|A Gentle Introduction
to the Backpropagation Algorithm and Feedforward Networks
Michael Wollowski and
||Students learn about feedforward neural
networks and the backpropagation algorithm by implementing a
perceptron network for AND and XOR Boolean functions and, given an
implementation of a feedforward network, learn digit recognition
using the MNIST data set.
2018 EAAI-2018: The Eighth
Symposium on Educational Advances in Artificial Intelligence, New Orleans, Louisiana, USA (collocated with
February 3-4, 2018
|Go for a Walk
||Real-world path planning for pedestrians. In
the first part, students implement A* over a map that includes
roads/paths as well as elevations. In the second part, students
collect actual data through walking around the real world, and the
cost model is then learned via regression techniques.
Solve a Maze via Search
||A natural extension to
Project's Search assignment, this assignment involves
formulating maze-solving as a search problem, image processing
(via OpenCV) as a step in maze-solving, as well as guided
performance/quality analysis of representational parameters.
|A Module on
Ethical Thinking about Autonomous Vehicles in an AI Course
Heidi Furey and Fred
||Within the context of an artificial intelligence
course, students are taught to identify ethical issues within
technical projects and to engage in moral problem solving with
regard to such issues.
Networks for Face Recognition with TensorFlow
||Students build feedforward neural networks for
face recognition using TensorFlow. Students then visualize the
weights of the neural networks they train. The visualization
allows students to understand feedforward one-hidden layer neural
networks in terms of template matching, and allows students to
How Recurrent Neural Networks Model Text
and Renjie Liao
||Students extend and modify existing code to
generate "fake English" text from an RNN. Students explore how the
RNN model is able to generate text that resembles the training
text by analyzing the weights and architecture of the RNN.
Optionally, students train the RNN themselves using a corpus of
Shakespeare plays as the training set.
||In this assignment
students learn how to control a robot to juggle a ball by
programming a velocity-controlled robot, such that it causes the
ball to bounce with some desired periodic motion. Specifically,
students will implement a hybrid controller that uses a mirror
control law within the framework of a 2D physics simulator.
Computing: Several Variants of a Universal Paradigm
||This assignment allows students to practice
with logic programming and constraint programming in Prolog using
a paradigm we call "biductive computing.", i.e. mixing deductive
and abductive computing. This assignment includes four variants of
biductive computing: database querying, planning, parsing, and
The Seventh Symposium on Educational Advances in Artificial Intelligence,
San Francisco, California, USA (collocated with
AAAI-17), February 5-6, 2017
to Monte Carlo Techniques in AI - Part II
||Learn about Bayesian network reasoning with
Gibbs Sampling, a Markov Chain Monte Carlo technique, through
implementation and experimentation.
||Students build a PDDL
planning library of common tasks supported by the Git version
control tool, and make use of the Fast Downward planning engine to
produce plans consisting of Git actions that update a repository
to a specified goal state.
Hidden Markov Model Toolkit
||To facilitate learning about the application of
Hidden Markov Models (HMMs) to Natural Language Processing (NLP),
these assignments break the task into modular parts, allowing
customized assignment coverage.
to Behavior-Based Robotics
||Students implement a behavior-based simulated
tank agent in the AutoTank environment with a reactive
behavior-based architecture built using the Unified Behavior
for Everyone: Introduction to Classification and Clustering
Papalaskari, and Carol Weiss
||An introduction to Classification and
Clustering aimed at a beginner, non-technical student audience.
Materials for hands-on exercises and pre- and post-test quizzes
||Students develop a “human-like” pathfinding
technique by specializing a generic search algorithm with custom
action cost and heuristic cost functions. Students apply classical
search algorithms and reflect on example organic paths to achieve
and Sertac Karaman
||Students use Image Based Visual Servoing to
program a mobile robot to park in front of solid color objects,
with control based on input from a standard monocular camera.
Students leverage open source software tools like OpenCV and ROS
to implement this reactive planner.
2016 EAAI-2016: The Sixth
Symposium on Educational Advances in Artificial Intelligence, Phoenix, Arizona, USA (collocated with
AAAI-16), February 13-14, 2016
The Fifth Symposium on Educational Advances in Artificial Intelligence,
Quebec City, Quebec, Canada (collocated with
July 28-29, 2014
2013 EAAI-2013: The Fourth
Symposium on Educational Advances in Artificial Intelligence,
Bellevue, Washington, USA (collocated with
AAAI-13), July 15-16, 2013
2012 EAAI-2012: The
Third Symposium on Educational Advances in Artificial
Intelligence, Toronto, Ontario, Canada (collocated with
AAAI-12), July 23-24, 2012
The Second Symposium on Educational Advances in Artificial Intelligence,
San Francisco, California (collocated with
AAAI-11), August 9-10, 2011
an introduction to satisfiability reasoning
||The popular boardgame
Cluedo) serves as a fun focus problem for this introduction to
propositional knowledge representation and reasoning.
||As a final project for an introductory AI
course, students work in small groups to design a scalable
guessing algorithm for the game of Mastermind. There are three
"challenges": a fixed-size problem, a "scalability challenge" ,
and a "learning challenge".
||This assignment is appropriate for a course
with a reinforcement learning component (i.e., Introductory AI or
Machine Learning). In it, students are asked to implement, test,
and evaluate multiple reinforcement learning algorithms within the
Generalized Mario domain.
2010 EAAI-2010: The
First Symposium on Educational Advances in Artificial
Intelligence, Atlanta, Georgia (collocated with AAAI-10), July 13-14, 2010
Projects Software Package for Introductory Artificial Intelligence
||The Pac-Man projects apply an array of AI
techniques to playing Pac-Man.
Project on Fast Trajectory Replanning for Computer Games for
"Introduction to AI" Classes
||In this project, the students need to code A*
and then extend it to Adaptive A*. Adaptive A* is a fast path
replanning algorithm which moves game characters in initially
unknown gridworlds to a given target.
|Getting Set with
||This assignment asks students to build a
program that plays the game of Set, making use of the OpenCV
library, the largest and most ubiquitous software foundation for
real-time vision processing.
||With Rook Jumping Maze generation as the fun
central challenge, students integrate techniques from uninformed
search, stochastic local search, and machine learning.
|Assignment on CSPs
for first undergraduate AI course
||This assignment covers most of the basic
principles and techniques involved in solving constraint
satisfaction problems, with applications ranging from games
(sudoku) to configuration and scheduling problems.
|A Project on
Gesture Recognition with Neural Networks for “Introduction to
Xiaoming Zheng, Sven Koenig
||In this project, the students need to
understand and extend an existing implementation of the
back-propagation algorithm and use it to recognize static hand
gestures in images.
||In this assignment, you will work with
partially-completed code for a genetic algorithm, adding
crossover, selection and mutation operators.
on Any-Angle Path-Planning for Computer Games for "Introduction to
||Sven Koenig, Kenny Daniel, Alex Nash
||In this project, the students need to code A*
and then extend it to Theta*. Theta* is an any-angle path-planning
algorithm which plans paths for game characters in known
1, 2018: Author AAAI website registration and
abstract submission deadline
5, 2018: Author electronic
assignment submission deadline
- November 1, 2018: Author notification
- November 14, 2018: Final assignment abstract and assignment
- January 28-29, 2019: Symposium
What is the Model AI Assignments Session?
Model AI Assignments Session seeks to gather and disseminate the best
assignment designs of the Artificial Intelligence (AI) Education
One must learn by doing the thing; for though you think you
know it, you have no certainty, until you try. - Sophocles
Recognizing that assignments form the core of student learning
experience, we invite AI educators to submit draft assignment
materials that exemplify an approach to teaching AI topics at all
Consider these challenges in assignment design:
- Intro AI audience: Submit your favorite assignment that
grounds one of the core AI concepts at the introductory level (e.g.
search, constraint satisfaction, knowledge representation and
reasoning, planning, probabilistic reasoning, machine learning,
robotics, machine vision, etc.).
- K-12/CS1/CS2 audience: Some AI assignment experiences are
designed to communicate the techniques, potentials, and challenges
of the discipline. Submit an assignment that you believe will be
most likely attract the next generation of AI practitioners.
- Emerging topics: When a new algorithm has high impact in
a research area, there is a need to introduce the algorithm not only
to students, but to all AI researchers as well. The creation of
initial high- quality assignments to teach such groundbreaking
techniques can accelerate research advancements and keep AI material
fresh at all levels. Submit an assignment which introduces an
recent new algorithm or emerging subfield.
Whether sharing the best of your time-tested assignment designs, or
offering a timely new creation, please consider how your creative
assignment ideas can attract and prepare the next generation of AI
researchers, or accelerate the advancement of the current generation.
The Model AI Assignment Session is a part of EAAI-19: The Ninth Symposium on Educational Advances in Artificial
Intelligence. This will be held January 28-29, 2019 in conjunction with AAAI-19
in Honolulu, Hawaii, USA.
To register for EAAI-19, participants can register for AAAI-19
(https://www.aaai.org/Forms/aaai-registration-form.php) and include
EAAI-19 as an additional (free) item in their registration cart.
Participants may also register for EAAI alone.
Model Assignment Considerations
As with SIGCSE's Nifty
Assignments, EAAI Model AI Assignments should be:
- Adoptable - Provide materials to make the assignment easy
for other instructors to adopt. Materials might include
handouts in common formats (e.g. HTML, PDF), starter source code,
data files, suggestions for use, etc.
- Engaging - Model assignments often have a playful "fun
factor" or impressive outcome. The applications spark interest in the topic, lead to deeper understanding, and are
accomplishments likely to be shared with others.
- Flexible/Scalable - Language/platform independence, while
not required, would allow more widespread use over time.
Suggestions of possible follow-on projects, further readings, or
other open ends invite continued learning. Advise for
variations in assignment design can help other instructors create
unique (i.e. not easily plagiarized) assignment experiences for
Model Assignment submissions must be made in two parts:
Assignment submission directions: Create a directory named
with your last name, a hyphen, and the name of the assignment (e.g.
Dodds-SetOpenCV). Place all of your materials in this
directory, and create an index.html file. (Our goal is to
create an archive of assignments, rather than link to resources
elsewhere. We will gladly update materials in our archive as
authors direct.) In this index.html
file, create a table like the one below filled in with brief information
about your assignment (replace the italicized text with your own):
||Describe your assignment
in a few sentences here.
||List the AI topics
relevant to the assignment.
||Describe the intended
audience (e.g. Introduction to AI, K-12, Advanced AI)
||Describe the perceived
assignment difficulty and time needed for the audience to
complete the assignment.
||Describe the assignment
||Describe the assignment
||List the necessary
prerequisite topic knowledge. Describe computing
requirements, such as necessary operating system(s) and/or
programming language(s), if any.
||In a few sentences,
describe possible ways in which other instructors can vary
the assignment, learn from its design, and/or encourage
follow-on assignment work.
Then add links to the assignment materials in your directory
(e.g. handouts in common formats (e.g. HTML, PDF), starter
source code, data files, suggestions for use, etc.). These
need not be polished for submission; a draft is sufficient.
Assignments should be anonymous for blind review. (Your
directory will be renamed with a number before review.)
Finally, zip the directory (e.g. Dodds-SetOpenCV.zip), and
email it to Todd Neller (firstname.lastname@example.org)
with the subject line "Model Submission: " followed by the filename.
(Please add "Model" to the subject line for all Model Assignments
Abstract submission instructions: Create a 200 word PDF abstract describing your assignment according to the
author instructions. Submit your anonymous abstract according to the
paper submission instructions on the
EAAI site via the
EasyChair submission site. Accepted submission abstracts will be
included in an extended abstract of the Model AI Assignments