UAI-01 Call for Papers

From: Daphne Koller (
Date: Tue Jan 16 2001 - 02:48:27 MET

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                          UAI-2001: Call for Papers
                              August 2-5, 2001
                          University of Washington
                          Seattle, Washington, USA

                             Conference homepage:

    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 include (but are not limited to):

      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?


      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

    Papers and abstracts should be submitted through
    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
    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.

    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


    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
    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.


    Please direct general inquiries to the General Conference Chair at Inquiries about the conference program and
    submission requirements should be directed to the Program Co-Chairs,
    Jack Breese and Daphne Koller, at

    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:
    Moisés Goldszmidt
    Peakstone Corporation
    155A Moffett Park Drive
    Sunnyvale, CA 94089

    Phone: +1 (408) 752-1024
    Fax : +1 (408) 752-1040

    Program Co-chairs:
    Jack Breese
    Microsoft Research
    One Microsoft Way
    Redmond, WA 98052

    Phone: +1 (425) 936-2969
    Fax: +1 (425) 936-7329

    Daphne Koller
    Computer Science Department
    Stanford University
    Stanford, CA 94305-9010

    Phone: +1 (650) 723-6598
    Fax: +1 (650) 725-1449
    E-mail: koller@CS.Stanford.EDU

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