CS378 Computational Intelligence in Game Design I

Spring 2011, ENS31NN, TTH 3:30-5pm, Unique number 53552

Uli Grasemann
Office hrs: ENS 31NN, after class and by appointment.


Adam Dziuk
Lab hours: ENS 31NN, Wed 1-3pm
Evaluation hours: ENS 31NN, 10:45am-12pm

Josh Brittain
Lab hours: ENS 31NN, Tue 2:00-3:30pm
Evaluation hrs: ENS 31NN, Thu 2-3:30pm

Peter Scamman
Lab hours: Mon ENS 31NN, 2-3:30pm
Evaluation hours: ENS 31NN, Thu 3:30-5pm


Interested in Game Programming or AI? UT's College of Natural Sciences and Department of Computer Science offer a class focusing on applications of Artificial Intelligence in games.

The class uses the NERO video game (www.nerogame.org), an award-winning game AI project that has been featured on Slashdot, Games Digest, AAAI, KXAN, and other venues; and OpenNERO (opennero.googlecode.com), an open source game platform for AI research and education featured in the most recent edition of Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig.

Students will become familiar with next-generation AI techniques that may play a central role in the next generation of video games and learn hands-on about working with modern game engines. The students will work in teams toward original research projects aiming to improve the state of the art of AI in games as well as to contribute to an active open source project.

There are no specific prerequisites, however you should be truly excited about working in some aspect of game technology and dedicated to making the team project a success.

Grading (tentative):

The class will be 3 credit hours. 75% of the grade is based on homework (each graded pass/fail), and 25% on a written project proposal (graded 0..100). Turning in the proposal or a homework late will reduce the grade 15% for the first 24hrs, 40% for the second, 75% for the third, and 100% after that. "Extra credit" on a homework is 15% (unless otherwise indicated in the assignment).

More details:

NERO development wiki
All current class information is posted here.

Class mailing list: cs378i@lists.utexas.edu

Other use ful links:
The NERO website
The UTCS Neural Networks Research Group

Wed Jan 25 12:30:00 CST 2011