UAI-2001: Call for Papers
August 2-5, 2001
University of Washington
Seattle, WA USA
Conference hompage:
http://robotics.stanford.edu/~uai01/
Uncertainty management is a key enabling technology for the
development of intelligent systems. Since 1985, the Conference on
Uncertainty in Artificial Intelligence (UAI) has been the primary
international forum for exchanging results on the use of principled
uncertain-reasoning methods in intelligent systems. The conference has
catalyzed advances in fundamental theory, efficient algorithms, and
practical applications. Theory and technology first presented at UAI
have been proven by their wide application in the scientific,
commercial, and industrial communities. The UAI Proceedings have
become a fundamental reference for researchers and practitioners who
want to know about both theoretical advances and the latest applied
developments in the field.
The scope of UAI is wide, covering a broad spectrum of approaches to
automated reasoning, learning, decision making and knowledge
acquisition under uncertainty. Contributions range from those that
that advance theoretical principles to those that provide insights
through the empirical study of applications, from quantitative to
qualitative approaches, from traditional to non-classical paradigms
for uncertain reasoning, and from autonomous systems to those designed
to support human decision making. We encourage submissions of papers
for UAI-2001 that report on advances in the core areas of
representation, inference, learning, decision making, and knowledge
acquisition, as well those dealing with on insights derived from the
construction and use of applications involving uncertain reasoning.
TOPICS OF INTEREST
Topics of interest include (but are not limited to):
Foundations
Representation of uncertainty and preferences
Theoretical foundations of uncertainty and decision-making
Uncertainty and models of causality
Semantics of belief
Revision of belief, combination of information from multiple sources
Higher-order uncertainty and model confidence
Relationships between different uncertainty calculi
Principles and Methods
Algorithms for reasoning and decision making under uncertainty
Automated construction of inference and decision models
Combination of models from different sources
Control of computational processes under uncertainty
Data structures for representation and inference
Decision making under uncertainty
Diagnosis, troubleshooting, and test selection
Enhancing human-computer interaction with uncertain reasoning
Explanation of results of uncertain reasoning
Formal languages to represent uncertain information
Hybridization of methodologies and techniques
Integration of other representation languages, including logic, with
uncertainty calculi
Markov decision processes
Methods based on probability, possibilistic and fuzzy logic, belief
functions, rough sets, and other formalisms
Method for learning models from noisy data
Multi-agent reasoning and economic models involving uncertainty
Planning under uncertainty
Qualitative methods and models
Reasoning at different levels of abstraction
Reinforcement learning
Representation and discovery of causal relationships
Resource-bounded computation (inference, learning, decision making)
Statistical methods for automated uncertain reasoning
Temporal reasoning
Time-critical decisions
Uncertain reasoning and information retrieval
Uncertainty and methods for learning and data mining
Empirical Studies and Applications
Comparison of representation and inferential adequacy of different calculi
Empirical validation of methods for planning, learning, and diagnosis
Experience with knowledge-acquisition methods
Experimental studies of inference strategies
Methodologies for problem modeling
Nature and performance of architectures for real-time reasoning
Uncertain reasoning in embedded, situated systems
For papers focused on applications in specific domains, we suggest that the
following issues be addressed in the submission:
Why was it necessary to represent uncertainty in your domain?
What are the distinguishing properties of the domain and problem?
Why did you decide to use your particular uncertainty formalism?
Which practical procedure did you follow to build the application?
What theoretical problems, if any, did you encounter?
What practical problems did you encounter?
Did users/clients of your system find the results useful?
Did your system lead to improvements in decision quality?
What approaches were effective (ineffective) in your domain?
What methods were used to validate the effectiveness of the system?
SUBMISSION INFORMATION
Deadlines:
Abstracts (200 words): Monday, March 12, 2001 (11:59PM PST)
Full papers: Tuesday, March 20, 2001 (11:59PM PST)
The deadlines will be strictly enforced (the submission server will
be closed at midnight). No extensions will be granted under any
circumstances.
Papers and abstracts should be submitted through
http://cmt.research.microsoft.com/UAI2001/
If authors have special circumstances that prevent electronic submission,
arrangements can be made directly with the program chairs below. Authors
are required to submit papers in the proceedings format. Submitted papers
must be no more than eight pages in proceedings format, including figures
and bibliography (about 5600 words). Accepted papers will be alloted
eight pages in the conference proceedings, with two additional pages
available for a fee. Please see
http://robotics.stanford.edu/~uai01/FormatInstructions.html
for format information and access to style files.
Papers submitted for review should represent original, previously
unpublished work. Papers should not be under review for presentation
in any other conference; however, an extended version of the paper may
be under review for publication in a scientific journal. Submitted
papers will be carefully evaluated on the basis of originality,
significance, technical soundness, and clarity of exposition. Papers
may be accepted for presentation in plenary or poster sessions. All
accepted papers will be included in the Proceedings of the Seventeenth
Conference on Uncertainty in Artificial Intelligence, published by
Morgan Kaufmann Publishers.
An outstanding student paper will be selected for special distinction at
UAI-2001. Please see the
for the requirements.
Other important dates:
Author Notification of Accepted Papers: April 30, 2001
Camera-ready Copy of Accepted Papers due: June 4, 2001
Workshops and Tutorials: Thursday, August 2, 2001
Technical Program: Friday, August 3-Sunday, August 5, 2001
AWARDS
This year, two outstanding papers will be selected for special distinction.
As usual, an outstanding student paper will receive the Best Student
Paper Award. Please see
http://robotics.stanford.edu/~uai01/StudentInstructions.html
for the requirements. In addition, for the first time, UAI-2001 will
consider awarding a Best Paper Award to an outstanding paper appearing in
the conference.
CONFERENCE ORGANIZATION
Please direct general inquiries to the General Conference Chair at
moises@cs.stanford.edu. Inquiries about the conference program and
submission requirements should be directed to the Program Co-Chairs,
Jack Breese and Daphne Koller, at uai01-pchairs@cs.stanford.edu.
CONFERENCE AREA CHAIRS
Greg Cooper, University of Pittsburgh
Adnan Darwiche, UCLA
Rina Dechter, UC Irvine
Didier Dubois, IRIT
Nir Friedman, Hebrew University
Danny Geiger, Technion
Lluis Godo, IIIA
David Heckerman, Microsoft Research
Eric Horvitz, Microsoft Research
Michael Jordan, UC Berkeley
Leslie Kaelbling, MIT
Uffe Kjaerulff, Aalborg University
Michael Kearns, AT&T Labs-Research
Michael Wellman, University of Michigan
CONFERENCE CHAIRS
General Conference Chair:
Moisés Goldszmidt
Peakstone Corporation
155A Moffett Park Drive
Sunnyvale, CA 94089
USA
Phone: +1 (408) 752-1024
Fax : +1 (408) 752-1040
E-mail: moises@peakstone.com
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Program Co-chairs:
Jack Breese
Microsoft Research
One Microsoft Way
Redmond, WA 98052
USA
Phone: +1 (425) 936-2969
Fax: +1 (425) 936-7329
E-mail: breese@microsoft.com
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Daphne Koller
Computer Science Department
Stanford University
Stanford, CA 94305-9010
USA
Phone: +1 (650) 723-6598
Fax: +1 (650) 725-1449
E-mail: koller@CS.Stanford.EDU
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