Homepage:http://jmvidal.cse.sc.edu

Office:SWGN 3A51

Office Hours:Check my Calendar, or email me for appointment.

Email:vidal@sc.edu

**Grading:**

Item | Percentage |
---|---|

Tests (2) | 25% each, for a 50% total. |

Final Project | 30% |

Problem Sets (3) | 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 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.

Jose M. Vidal Last modified: Wed May 20 17:04:50 EDT 2009