CSCE 475/875

Multiagent Systems


Class Syllabus

Fall 2007




Name:              Prof. Leen-Kiat Soh

E-mail:                                   Phone: (402) 472-6738

Office:             122E Avery Hall                                   Office Hours:    11:30 AM – 12:30 PM MWF

Class Time:       10:30 – 11:20 AM MWF                     Classroom:       118 Avery Hall



Teaching Assistant


Name:              Nobel Khandaker                                 E-mail:    

Office:              122F Avery Hall                                   Office Hours:    11:30  AM – 1:00 PM TR


Class Objectives


This class will introduce you to the research topic of multiagent systems (MAS), including what a MAS is, what agents are, and what are the disadvantages and advantages of such a system in what types of applications.  We will address issues in distributed problem solving and planning, search algorithms for agents, distributed rational decision making, learning in multiagent systems, computational organization theory, and formal methods in Distributed Artificial Intelligence (DAI).  We will also look into multiagent negotiations, emergent behaviors (such as ants and swarms), and Robocup technologies.  Time permitting, we will also look into research in real-time coalition formation. 


The course materials in this class are based on the textbook and journal/conference papers.


Required Background


Prerequisites: CSCE 310 (Data Structures & Algorithms required).


Text Book


G. Weiss, (Ed.), "Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence," MIT Press, 1999.




Final grades in this class will be assigned based on the following scale:


A:         94% - 100%

A-:       90% - 93%

B+:       86% - 89%

B:         80% - 85%

C+:      76% - 79%

C:         70% - 75%

D+:      66% - 69%

D:         60% - 65%

F:         below 60%


A+ is awarded to a student whose work and understanding of the class prove to be exceptional.


There will be about 12-13 collaborative topic summaries (25%), game days (group) (20%), one mid-term examination (20%), one seminar presentation (group) (10%), and one final project (group) (25%).  In general, students registered for CSCE875 will be given additional questions or activities for most of the assignments.    


Collaborative Topic Summaries


Collaborative topic summaries are written summaries of topics that we cover in class.  Specific requirements will be given before each summary assignment.  The students will be divided into several groups and each group will collaborate to prepare one final version of the topic summary.  The summary will be based on my lectures, the textbook, and other papers.  Your summary should include at least the following (a) an overview of the topic – motivations and underlying principles, etc., (b) a list of praises: a description of what you think are the important/useful aspects of the topic, (c) a list of critiques: a description of what you think are the weaknesses of topic, (d) a list of wishes: what areas of the topic do you think that should be improved, and (e) a list of questions on material that you did not understand from the lectures and reading materials.   You will also be required to respond to stupid questions.  The summary will be graded in the following manner:


10% Overview

            15% Praises

            15% Critiques

            15% Wishes

            15% Questions

            10% Response to Stupid Questions

            10% Grammar and Errors

            10% Requirements


The collaborative writing process will use I-MINDS, a multiagent software system developed here at the University of Nebraska.  I-MINDS will allow you to collaboratively prepare the topic summaries.  In particular, you will be able to: (1) Propose a topic summary section, (2) Reject a proposed topic summary section, (3) Revise a topic summary section, (4) Extend a topic summary section, and (5) Accept a proposed topic summary section.  Furthermore, you will also be able to view the latest version of the topic summary and post your comments about the sections of the topic summary in a forum that is viewable only to your group.  Once your group has completed editing the topic summary, you will view the latest edition of the topic summary and accept the final version. It is required that all members agree and accept the final version of the topic summary before the due date.  Otherwise, the whole group will incur a penalty in their final score of the topic summary.  Also, there should be no edition of the topic summary after all the group members have accepted the final version.  If the topic summary is edited after everybody has accepted it, all the group members will have to read and accept the final version again. 


All of your actions will be tracked and recorded while you are working on the topic summary.  After each topic summary is completed, you will be able to review your peers and your team and receive a grade for your contribution to the collaborative topic summary.  We calculate your contribution to the topic summary with the following formula:


     minimum(1, N*(w1*PS+w2*RE+w3*RV+w4*EX+w5*AC+w6*CM+w7*PR)/7)            (1)




PS = total number of propositions of topic summary sections a student makes divided by the total number of propositions of topic summary sections of his or her group members

RE = total number of rejections of topic summary sections a student makes divided by the total number of rejections of topic summary sections of his or her group members

RV = total number of revisions of topic summary sections a student makes divided by the total number of revisions of topic summary sections of his or her group members

EX = total number of extensions of topic summary sections a student makes by the total number of extensions of topic summary sections of his or her group members

AC = total number of acceptances of topic summary sections a student makes by the total number of acceptances of topic summary sections of his or her group members

CM = total number of comments posted regarding the topic summary sections a student makes divided by the total number of comments regarding the topic summary sections posted by his or her group members

PR = peer rating received by a student divided by the sum of peer rating received by his or her group members

N = size of the student group

W7=1.5, W1=W2=W3=W4=W6=1, and W5=0.5

The maximum value of the Equation 1 is capped at 1.0.


Notice that PR is normalized.  So, giving each other high peer ratings may lower the score of the group members.  Furthermore, at least half of a student’s propositions, revisions, rejections, extensions and acceptances should be regarding to other students’ submitted editions (unless the other group members have not contributed at all).  A student found not following this rule will incur a penalty in his or her topic summary score.


The member receiving the highest score in terms of collaborative activities will receive 100% of the grade of the summary submitted.  The other members’ grades will be proportional to their contribution (calculated using Eq. (1)) to the topic summary.  Thus, the maximum score received by a student is equal to the total points assigned to the topic summary and the minimum score is zero. 


Educational Study.  A formal study will be conducted to evaluate the impact of collaborative writing and I-MINDS group formation techniques on student learning.  Your decision to participate (or not participate) in this study will not impact your grades in the class. You will be asked to sign a consent form for us to use the data tracked using I-MINDS in this study.




The seminar presentation is for the students to present technical papers in the area of MAS, agents, and distributed AI.  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% Organization

            20% Conclusions: Comparisons, Insights, etc.

            15% Q&A and Participation


Final Project


The final project will be for a multiagent simulation that aims to show how local decisions made by a large group of agents (greater than 500) can lead to meaningful emergent behaviors that satisfy global goals.  The simulation software that we will use is Repast.  See the Resources page on our course website for the software.  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:


45% Program Correctness

15% Software Design

10% Programming Style

15% Testing

15% Documentation 


The report will be graded based on:


50% Methodology, Implementation, Results, and Conclusions

25% Organization

15% Requirements

10% Grammar and Errors


Game Days


There will be 3-4 special game days planned for the semester: Auction Day, Allocation Day, Coalition Day, and Negotiation Day.  On each of these days, you will be required to pit what you have learned in the class against your classmates in various contests.  You participation on those days will be evaluated.  Your written reports for those days will be counted as participation as well.  For this semester, each game day will likely to place over two lectures due to the number of groups and the time we have for each lecture.  Each game day will be graded in 2 parts: contest (50%) and report (50%).  Each group member receives the same score for his or her group.  We will also have a Game Days League – the winner of the league will get to sign the “Ball of Fame”.


The contest part will be graded in proportion to how your group ranks at the end of the game day.  For example, the winning team will be given N points; the second-ranked team will be given N-2 points; the third-ranked team will be given N-4 points; and so on.  N will be determined later in the semester depending on the number of groups we have in the class.


The report part will be graded roughly in the following manner:


            40% Pre-game strategies

            30% During-game observations and changes in strategies

            30% Post-game strategies and lessons learned




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. 


Academic Misconduct


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 student’s 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 another’s work or part of another’s 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 another’s 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 ( You are expected to know and abide by this policy.


Those who share their code and those who copy other’s 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.