Listed below are some of the graduate level courses taught by members of the artificial intelligence research group.
Game-theoretic Methods for Computer Science (CS 886)
In settings ranging from computer networks to
electronic markets, computer scientists are observing and designing
systems consisting of multiple self-interested parties (or agents).
Game theory and mechanism design provide us with a set of tools and
techniques for analyzing agent behavior in these systems, as well as
informing us how to design systems so that agents behave in the way we
would like them to.
Issues in Natural Language: Knowledge Representation (CS 886)
Image and Vision Computing (CS 498/698)
This course will provide an introduction to key game theory and
mechanism design concepts. We will study how these ideas are being
used in computer science, as well as computational issues in game
theory and mechanism design. Examples and applications from AI,
Theory, and Systems will be presented.
Offered Fall 2006.
This course covers topics such as image representation,
lightening and reflectance models, filtering, motion and object
recognition and indexing.
Intelligent Computer Interfaces (CS 785)
Offered Winter 2006.
This course provides an overview of several subtopics in
artificial intelligence, related by the theme of intelligent
interfaces. The first half of the course is united by a theme of
natural language processing and covers the subtopics of plan
recognition, discourse, natural language generation and user modeling.
We also introduce the topic of intelligent agents, including a
discussion of personalized recommender systems. In the second half of
the course, the focus shifts to examining several kinds of intelligent
systems and the interface issues surrounding these systems. We first
examine multi-agent systems, including a discussion of adjustable
autonomy systems and applications to e-commerce. Other systems
examined vary each year but have included such topics as information
retrieval, datamining, case-based systems, knowledge-based systems
(including the semantic web) and intelligent tutoring systems.
Systems (CS 886)
Offered Fall 2005.
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.
Reasoning Under Uncertainty (CS 886)
Offered Winter 2006
The design of automated systems capable of accomplishing
complicated tasks is at the heart of computer science.
Abstractly, automated systems can be viewed as taking inputs and
producing outputs towards the realization of some objectives.
In practice, the design of systems that produce the best
possible outputs can be quite challenging when the consequences
of the outputs are uncertain and/or dependent on other systems,
the information provided by the inputs is incomplete and/or
noisy, there are multiple (possibly competing) objectives to
satisfy, the system must adapt to its environment over time,
etc. This course will focus on the principles of probabilistic
reasoning and sequential decision making for a wide range of
settings including adaptive and multi-agent systems. The
modelling techniques that will be covered are quite versatile
and can be used to tackle a wide range of problems in many
fields including robotics (e.g., mobile robot navigation,
control), computer systems (e.g., autonomic computing, query
optimization), human-computer interaction (e.g., spoken dialog
systems, user modelling), bioinformatics (e.g., gene sequencing,
design of experiments), operations research (e.g., resource
allocation, maintenance scheduling, planning), etc. Hence, the
course should be of interest to a wide audience beyond
Electronic Market Design (CS 886)
Offered Fall 2005.
This course covers topics on the design and analysis of
electronic market places. This is an exciting new research
area which incorporates ideas from economics (in particular
game theory and mechanism design), AI, and theoretical
computer science. Electronic markets have many interesting
applications, from the obvious ones such as automated
negotiation for ecommerce, to more non-obvious applications
like resource allocation in grid computing settings. In this
course we will focus on computational and game-theoretic
questions related to electronic markets, and will look at
what it means to design electronic markets with good properties.
Natural Language Computing (CS 886)
Statistical Learning Theory (CS 886)
Offered Fall 2004.
Machine learning is a fast growing topic of both academic
research and commercial applications. It addresses the issue of
how can computers "learn", that is, how can processes drawing
useful conclusions from massive data sets be automated. Machine
learning plays a central role in a wide range of important
applications emerging from need to process data sets whose sizes
and complexities are beyond the ability of humans to handle.
Computational Vision (CS 787)
Introduction to Artificial Intelligence (CS 686)
Offered Winter 2005. This course will be offered in Winter 2006
as CS 498/698.
This course is cross-listed with CS 486.