COSC 387: Artificial Intelligence
Project Guidelines
This research project is designed to let you concentrate on a problem
of interest that is related to AI. It consists of three assignments,
two of which are graded:
the prospectus,
the proposal, and
the final paper.
You are certainly not limited to the following, so be creative.
However, you must do something computational.
A list of the types of projects that would be acceptable includes:
Implementations
If you have solid programming skills, then you could attempt to implement
a learning algorithm or a theorem prover. You would need to apply it
to a few simple problems to demonstrate that it works. For example,
one student implemented the AQ learning algorithm in Java and applied
it to the mountain bike and iris data sets.
Experimental Studies
You could also take advantage of the many implementations that exist
and conduct an empirical study of how they perform for a set of problems.
For example, you could determine which machine learning technique
best predicts how representatives in Congress vote.
Technique X Applied to Problem Y
There are many opportunities to build systems using AI techniques.
A lot of people are making money as Fung Shei consultants. It
might be interesting to see if you could use techniques based
on diagrammatic reasoning to build a Fung Shei expert system
that would arrange furniture in a room based on the principles
of Fung Shei. As another example, one student attempted to
use decision trees to predict the winner of NBA basketball games.
And I have had two students build programs for dream interpretation
using rules to encode the mapping from symbols to interpretation.
Still Stuck?
If you're still having difficulty converging on a topic, come see
me. I'll be happy to hand you a project. I have a lot of data,
a lot of code, and a lot of unanswered questions. Here are some
possibilities (aka shameless plugs):
- I have a collection of face images, and I need an eye detector.
- I have a collection of building images and an implementation of
a linear feature detector, and I need it optimized for the images,
and I need an implementation of a corner detector.
- I'm interest in concept drift, which are concepts that
change over time. There are a few on-line learning algorithms for
which it'd be interested to see how well they perform on a set
of drifting concepts.
- I have some audit trail data for a computer intrusion detection
task, and I have some ideas about an experiment that would compare
two prevailing approaches.
- There is an unsupervised learning method called ``hypergraph
clustering.'' Learn about it, implement it, and compare it to
existing approaches, like $k$-means, using some, say, gene data.
