Advanced Topics in AI
Instructor: Kate Larson
Room: DC 3313
Schedule: Tuesdays and Thursdays 10:00-11:30
Results from the tournament are found here!
On Tuesday, April 11 we will have a series of presentations on
everyones projects. The goal of these presentations is to give others
in the class a highlevel idea what you are working on. Each student
(or group if applicable) has approoximately 5-7 minutes to talk.
**There is no need to make slides.** Also, we will start later than
normal. The class will start at **10:30** instead of 10:00.
Classes on March 24, April 4, and April 11 will be held in DC 3314.
Assignment 3 has been posted.
I have updated the schedule. For the week of March 20th, we are having 6
presentations. You only need to submit reviews on two of the days.
Class for February 16th is cancelled, due to the University closure. On
February 21 we will start with the class presentations. This means that
we will skip the presentation on Multiagent Learning. I will be posting
some notes and slides on single agent reinforcement learning so that
students who have not done an AI course, will not be lost when we read the
Notes on MDP's
Notes on single agent reinforcement learning
Assignment 2 is due on February 28th.
Here are some resources that you will find useful when doing Assignment 2.
Assignment 2 has been posted.
Here is an example of a Bayes Nash equilibrium. (pdf).
Here is an example of a subgame perfect equilibrium in a bargaining game
Assignment 1 can be found here.
Office hours for CS 886 are on Tuesdays from 2:30-3:30.
The field of multiagent systems studies systems of multiple autonomous
entities with diverging information and perhaps interests. This
creates challenges above and beyond single-agent settings since we
must now be additionally concerned with such issues as cooperation,
coordination, and overcoming self-interest of individual
agents in order to reach desirable system-wide goals.
This course covers the mathematical and computational foundations of
multiagent systems, with a focus on game theoretic analysis of systems
in which agents can not be guaranteed to behave cooperatively.
This course draws on a wide set of ideas from AI, CS theory and
economics. While there are no formal prerequisites, some of the
topics are quite formal mathematically, and students need to be able
to construct and follow formal proofs.
Please send me
email if you have any questions.
Course Topics (tentative list)
Distributed problem solving
- Games (normal-form, extensive-form, repeated, stochastic, Bayesian)
- Computation of game theoretic solution concepts
- Bounded rationality
- Social choice
- Mechanism design
- Auctions (single item, combinatorial)
- Teams and coalitions
- Multiagent learning
The course will be a combination of lectures and
reading and discussion of research papers. Students will be given
several homework assignments on the material covered in the
lectures. With the research papers, students will be responsible for
presenting them in class and discussing them. Projects will also be
presented in class at the end of the semester.
participation is an important component of this course. Before each
class, all students must read the paper and submit comments and
questions. Things to think about include
- What is the main contribution of the paper?
- Is it important? Why?
- What was the main insight of the paper?
- What assumptions were made?
- What applications might arise from the paper?
- How can the results be extended?
- What was unclear to you?
The final project allows students to explore material not covered in
class, and share that material with other students. The topic of the
project can be a survey of a subarea of multiagent systems, a compare
and contrast study of two or more influential papers, or a
development of your own research ideas. Possible ideas for projects
will be discussed in class.
The project will involve several steps
- Project proposal
- Project presentation
- Final project report