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 (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.

Jose M. Vidal Last modified: Wed Aug 11 16:15:32 EDT 2004