Advanced Multiagent Systems
Instructor & Info
Professor Leen-Kiat Soh
122E Avery Hall
9:30 – 10:30 AM and Open Door Policy
1:30 – 2:20 PM MWF
Study of advanced multiagent systems (MAS) in theory, applications, and connections to other AI disciplines, notably in machine learning. The course is a hybrid of seminar-based presentations with follow-up discussions and project-based. Involve developing and implementing MAS solutions for real-world problems or simulations.
Prerequisites: Graduate standing at the University of Nebraska. Background in artificial intelligence (AI) or MAS is also preferred.
Optional: Shoham, Y. and K. Leyton-Brown (2010). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press.
Course Objective Overview
The primary objective is to investigate advanced MAS issues such as team formation, multiagent learning, and multiagent meta-reasoning. Other topics of interests include negotiations, knowledge representation, modeling, and resource allocation. The second objective is to practice MAS-based problem solving skills and techniques through hands-on experience.
The course schedule will be determined in the first week after our first meeting of the semester.
Final grades in this class will be assigned based on the following scale:
A: 94% - 100%
A-: 90% - 93%
B+: 87% - 89%
B: 83% - 86%
B-: 80% - 82%
C+: 77% - 79%
C: 73% - 76%
C-: 70% - 72%
D+: 67% - 69%
D: 63% - 66%
D-: 60% - 62%
F: below 60
A+ is awarded to a student whose work and understanding of the class prove to be exceptional.
There will be about in-class participation (10%), Wiki participation (10%), seminar presentations (20%), and one final project (group) (60%).
In-class participation includes attendance and active contributions to the seminars and discussions. These will be subjectively graded by myself. Overall, if you attend classes and contribute regularly, I expect that you will receive the full 10% credit.
For each seminar, I will setup a Wiki essay thread on an online software Wiki environment.
Students are then required to contribute. These will be graded based on:
20% Discussions (Forum)
The seminar presentation is for the students to present technical papers in the areas of multiagent systems—both theories and applications—and relevant machine learning topics. A list of papers will be provided to the students. Each presentation will involve a Q&A session paneled by the presenters and moderated by the instructor; and all groups are required to participate in Q&A as well. These seminar presentations will be graded based on:
50% Summary of Paper
20% Conclusions: Comparisons, Insights, etc.
15% Q&A and Participation
The final project will be for design and implementation of a system (e.g., a simulation or an adaptive system or a solution for a MAS-based contest) that aims to show how MAS and multiagent learning paradigms can be used to improve the quality (effectiveness and/or efficiency) of the system performance. This assignment will be graded in 2 parts: programming (50%) and report (50%). Each group member receives the same score for his or her group. The programming part will be graded based on: (1) 45% Program Correctness (also demonstration), (2) 15% Software Design, (3) 10% Programming Style, (4) 15% Testing, and (5) 15% Documentation
The report will be graded based on: (1) 50% Methodology, Implementation, Results, and Conclusions, (2) 25% Organization, (3) 15% Requirements, and (4) 10% Grammar and Errors
Students with disabilities are encouraged to contact Christy Horn for a confidential discussion of their individual needs for academic accommodation. It is the policy of the University of Nebraska-Lincoln to provide flexible and individualized accommodation to students with documented disabilities that may affect their ability to fully participate in course activities or to meet course requirements. To receive accommodation services, students must be registered with the Services for Students with Disabilities (SSD) office, 132 Canfield Administration, 472-3787 voice or TTY.
Violations of academic integrity will result in automatic failure of the class and referral to the proper university officials. The work a student submits in a class is expected to be the studentŐs own work and must be work completed for that particular class and assignment. Students wishing to build on an old project or work on a similar topic in two classes must discuss this with both professors. Academic dishonesty includes: handling in anotherŐs work or part of anotherŐs work as your own, turning in one of your old papers for a current class, or turning in the same or similar paper for two different classes. Using notes or other study aids or otherwise obtaining anotherŐs answers for an examination also represents a breach of academic integrity. Sanctions are applied whether the violation was intentional or not.
Academic dishonesty of any kind will be dealt with in a manner consistent with the CS&E Department's Policy on Academic Integrity (http://cse.unl.edu/undergrads/academic_integrity.php). You are expected to know and abide by this policy.
Those who share their code and those who copy otherŐs code will be penalized in the same way; both parties will be considered to have plagiarized.
To help avoid these problems, please start assignments early and seek help when you need it.
PLAGIARISM OF ANY KIND IN THIS COURSE WILL RESULT IN A GRADE OF F.