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 two
assignments, one of which is graded:
the prospectus,
the final paper.
When submitting these documents, hard copies are fine. If you
want to submit electronically, then I accept only documents
in Adobe's
Portable Document Format (PDF).
You can use the TOM Server
to convert documents to PDF.
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. You could also extend
one of the programming projects. For instance, you could extend
project 3 by implementing a theorem prover for first-order logic.
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. One student found
a data set from a competition for predicting electicity demand and
used implementations of various machine-learning algorithms in an
attempt to best the winner of the competition. Plus, predicting
demand for electicity is kind of important. Just ask any Californian.
Technique X Applied to Problem Y
There are many opportunities to build systems using AI techniques.
For instance, 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.
Finally, two students have built 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. My interests
lie in the area of machine learning, so if you're intrigued by
that, here are some possibilities (aka shameless plugs):
- I'm interested 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.
