Homepage: http://jmvidal.cse.sc.eduHomepage: http://jmvidal.cse.sc.edu/csce782/
Office: SWGN 3A51
Office Hours: Check my Calendar, or email me for appointment.
|Tests (2)||25% each, for a 50% total.|
|Problem Sets (4)||5% each, for a 20% total.|
We will adhere USC's statement on academic responsibility. This means that expulsion procedures will be initiated for anyone caught either giving or receiving help in a problem set or test. I will be grading everything myself since this class does not have a TA. Please, try to help out by properly commenting your code.
Problem Sets: All problem sets are to be done individually and will likely involve the use of netlogo to solve a multiagent problem. All problem sets will be graded based on the quality of the writeup up: the quality of the writing, the originality of the ideas use, the simplicity of the code, and the performance of the system.
Tests: There will be two tests. They cover the material discussed in class.
Final Project: The final project will consist of writing a research paper. The project can be done individually or by a pair of students; bigger groups are disallowed.
Overview: This class will provide a solid foundation in the field of multiagent systems design and engineering. We study all the major MAS design techinques, agent architectures, and communication languages. We take a hands-on approach by building many NetLogo simulations of well-known problems.
The class, therefore, has two components: theoretical and implementation. The theoretical component includes the lectures, readings from the textbook and papers, and several problem sets. The implementation component includes the programming assignments and final project.
Prerequisites: You will do better in this class if you have taken an introductory AI class and possess some mathematical sophistication.
Deliverables: Students who pass this class are able to design and implemented complex solutions for distributed, real-time, noisy problems that require the coordination of independent and possibly selfish autonomous units. The students have in-depth knowledge of the most common agent architectures, coordination protocols, and the mathematics required to understand coordination, cooperation, and mechanism design. They also have basic knowledge of game theory and economic theory as they apply to the design of incentive-compatible protocols.