Instructor: José M. Vidal
Time: MW 10:10AM-11:00AM (no class meeting on Fridays)
Room: SWGN 2A18
Textbook: Fundamentals of Multiagent Systems, by Jose M. Vidal. A draft of the textbook I am writing. You can also download Multiagent Systems, by Shoham & Leyton-Brown
Problem Sets: 60%
Final Project: 40%
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.
Final Project: The final project will consist of presenting to the class a research paper from the literature and then either implementing (part-of, related stuff) of the paper in NetLogo or extending the ideas presented in the paper.
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 models, coordination protocols, and the mathematics required to understand coordination, cooperation, and mechanism design. They also have in-depth knowledge of game theory and economic theory as they apply to the design of incentive-compatible protocols.