E6899 Autonomous MultiAgent Systems
Department of Electrical Engineering, Columbia University
This is the course homepage for EECS E6899: Autonomous MultiAgent Systems. Basic information
Course announcements4/21/20: Project presentations will now be 7minute flash presentations. Details on Courseworks 4/20/20: Final report deadline extended to 11:59pm EST, 12 May 4/2/20: Presentation schedule updated 3/26/20: Presentation schedule updated 3/19/20: Presentation schedule updated 3/19/20: Midterm project report deadline extended to April 3rd 3/9/20: Class on Tuesday March 10th is cancelled. From Thursday, classes will be online. URL to follow... 2/26/20: Fixed typo in the "Graphs" slides  updated version is now on Courseworks 2/26/20: Presentation schedule locked (see below for speakers and papers) 2/25/20: Updated the reading list 1/28/20: Presentation signup sheet posted (see Quick links below) 1/24/20: Updated the reading list to include power and energy papers 1/21/20: Please take the background survey www.tinyurl.com/tb4kcq8 1/10/20: Draft reading list posted (see schedule section below) 1/9/20: Tentative schedule posted 12/16/19: Prof Goldfarb will be teaching EEOR E6616: Convex Optimization  it would be a great complement to this class 11/22/19: Course website created Quick links
OverviewThis graduatelevel topics course will introduce students to the mathematical foundations of modeling, control, and analysis of networked dynamical systems (otherwise known as multiagent systems). We will investigate how dynamical systems interact with each other over a network, and how such systems can be analyzed and controlled, in particular how the network structure affects achievable performance. One of the central aims of the course is to understand and prove fundamental properties about multiagent systems. As such, our focus will be on simple (but informative) models that we can concisely reason and construct rigorous statements about. Classes will be a mixture of instructorled lectures, student presentations, and discussions. Assessment will be via paper presentations and a class project (more details below). ScheduleThis schedule is subject to change. Note there is no class on March 17th & 19th. The papers referenced can be found here. In some cases, two papers are listed, the first paper will provide the necessary background for the second paper (which is the paper being presented). Pdfs of the papers are also available on Courseworks. If you are presenting a paper and would prefer that we prepare for your presentation by reading a different paper, let me know and I'll update the details below.
Course organizationPrerequisitesThere are no specific course prerequisites for this class beyond mathematical maturity. Students will be expected to read, understand, and present mathematical proofs. A strong working knowledge of linear algebra and calculus will be assumed. Previous classes in control (linear systems theory) and convex optimization would be helpful but are not necessary. Undergraduate students will require instructor's permission. As a guide to determining whether you have the appropriate mathematical background, download the following documents: The content of the linear algebra notes should be familiar to you. Some of the dynamical systems material may be new, but you should find this straightforward to follow. After the second class it will be taken for granted that this material is known and understood. Class structureDuring the first few weeks of class the instructor will cover the necessary background material on dynamical systems, graph theory, and optimization. For each of the core theoretical topics (listed above), the instructor will teach one class, following which there will be studentdriven lectures based on paper presentations and class discussions. The remainder of the time will be spent on studentchosen applications that link to the previously studied theory. GradingGrading will be based on three components:
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