Invited Speakers
We are pleased to announce the following invited talks
for the 8th Symposium on Artificial Intelligence
and Mathematics, January 46, 2004 in Fort Lauderdale,
Florida.

Robert Bixby, ILOG Inc.
The New Generation of MixedInteger Programming Codes
Dantzig, Fulkerson, and Selmer Johnson laid the foundation for
modernday integer programming computation in their 1954 paper using
linear programming methods to solve a certain 49city traveling
salesman problem. In the 1960s, Gomory made important theortical
contributions to the subject, and in the 1970s the practical use of
integer programming took a significant step forward with the
introducion of two powerful, commercial integer programming codes:
MPSX/370 (IBM) and UMPIRE (SCICONIC). In the years that followed,
integer programming computational research flourished, with important
work by Groetschel, Ellis Johnson, Padberg, Wolsey and others.
However, virtually all of that work remained isolated in largely
academic codes. From the mid 1970s through the late 1990s, commercial
integer programming codes got faster, but only because linear
programming and computing machines got faster; the
underlying integerprogramming methodolgies remained essentially
unchanged. In the last several years, that situation has changed
dramatically, with the development of a newer generation of commerical
codes incorporating most of the important computational advances from
the previous two decades. The real achievement of these codes, and a
central theme of this talk, is the integration of a wide collection of
methods and ideas in one algorithmic framework with robust default
behavior, capable of solving a significant fraction of integerprogramming
instances encountered in practice.

Ronen Brafman, BenGurion University, Israel
PreferenceBased Constrained Optimization with CPNets
Consider the problem of providing users with tools that enable optimal
selection of configurable products, such as, vacation packages, personal
computers, or even a personalized newspaper. These configuration problems
can be formulated abstractly as constrainedoptimization problems: Find the
best (given the users preferences) vacation satisfying various constraints
(duration, price, etc.). This class of constrained optimization problem,
which we refer to as "preferencebased constrained optimization" differs
from the classical constrained optimization problems studied in applied
mathematics and OR. Rather than realvalued variables we are mostly dealing
with discrete variables with finite domains, and instead of realvalue
objective functions, we have the amorphous notion of "user preferences". To
formulate this class of problems properly, we need to more carefully specify
their input, bearing in mind that this information must be realistically
obtainable from the type of users we have in mind. We must also provide
efficient solution algorithms, so that this approach is usable in online
applications. In this talk I will describe CPnetworks, a
knowledgerepresentation structure that exploits the notion of conditional
preferential independence to enable convenient preference elicitation and
representation. I will explain their basic properties and I will show how we
can do preferencebased constrained optimization given a CPnet efficiently.
To illustrate these ideas, I will describe and demonstrate a tool we
recently built for personalization of synchronized richmedia presentations.

Henry Kautz, University of Washington, USA
Toward a Universal Inference Engine
In the early days of AI some researchers proposed that intelligent
problem solving could be reduced to the application of general purpose
theorem provers to an axiomatization of commonsense knowledge.
Although automated firstorder theorem proving was unwieldy, general
reasoning engines for propositional logic turned out to be
surprisingly efficient for a wide variety of applications. Still many
problems of interest to AI involve probabilities or quantification,
and would seem to be beyond propositional methods. However, recent
research has shown that the basic backtrack search algorithm for
satisfiability generalizes to a strikingly efficient approach for
broader classes of inference. We may be on the threshold of
achieving the old dream of a universal inference engine.