[UAI] CFP: Machine Learning as Experimental Philosophy of Science

From: Kevin Korb (korb@csse.monash.edu.au)
Date: Thu Mar 15 2001 - 14:09:32 PST

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    ----------------------------------------------------------------------
                               Call for Papers
    ECML Workshop: Machine Learning as Experimental Philosophy of Science
                 2001 European Conference on Machine Learning
                     Freiburg, Germany, 3 September 2001
    ----------------------------------------------------------------------

    Machine learning studies inductive strategies in algorithms. The
    philosophy of science investigates inductive strategies as they appear
    in scientific practice. Although the two disciplines have developed
    largely independently, they share many of the same issues. This is
    slowly coming to be recognized in a number of ways, as evidenced in
    the annual Uncertainty in AI and AI and Statistics conferences. This
    workshop will explore the extent to which the methods and resources of
    philosophy of science and machine learning can inform one another.

    In "Computational Philosophy of Science" (1988) Paul Thagard presented
    a challenge to the philosophical community: philosophical theories of
    scientific method, if they are worth their salt, should be
    implementable as computer programs. In this workshop we will address
    this challenge and also the inverse challenge to machine learning
    researchers: both machine learning algorithms and methods for
    evaluating machine learning algorithms should be implementations of
    sensible approaches to philosophy of science. Machine learning
    researchers have only recently discovered the relevance of statistics
    and philosophical views on the foundations of statistics to evaluating
    the performance of their systems; we hope this workshop will carry
    that discussion further.

    The workshop will therefore focus on such questions as:

      1. Can machine learning experiments tell us about inductive discovery in
    science?

      2. What theoretical results in computational learning can be useful
    in understanding scientific methods? How can accounts of scientific
    confirmation, explanation, discovery and consilience be used to
    develop automatic learning systems?

      3. How can we assess induction? What statistical or other criteria
    need to be met to prefer one machine learning algorithm and/or
    scientific method over another? What is the role in machine learning
    and science of model building versus prediction?

      4. Is there a substantial difference between scientific reasoning as
    conceived in the philosophy of science and in machine learning?

      5. Is scientific method indeed mechanizable? Are scientific
    practices algorithmic?

    Note: ECML will be co-located with PKDD 2001 -- the European Conference
    on the Principles and Practices of Knowledge Discovery in Databases.
    For more details see:
      http://www.informatik.uni-freiburg.de/~ml/ecmlpkdd/

    ++++++++++++++++
    Invited Speakers
    ++++++++++++++++

    Professor Kevin Kelly (CMU, Philosophy), author of "The Logic of
    Reliable Inquiry (Oxford, 1996). His recent work concerns reliable
    belief revision, the solution of methodological regresses, and
    efficient convergence.

    Dr Peter Flach (Bristol, Computer Science), co-editor of "Abduction
    and Induction: essays on their relation and integration" (Kluwer,
    2000) and co-organiser of workshops on Abductive and Inductive
    Reasoning in AI at ECAI'96, IJCAI'97 and ECAI'98.

    +++++++++++
    Publication
    +++++++++++

    Accepted papers will be published in the first instance as workshop
    notes and on the web. Authors are invited to revise their articles in
    the light of the discussions at the workshop and submit them to a
    special issue we have arranged with the Journal for Experimental and
    Theoretical Artificial Intelligence.

    ++++++++++++++++
    Important Dates:
    ++++++++++++++++

    Papers due: 8 June 2001
    Notification: 29 June 2001
    Camera-ready due: 13 July 2001
    Workshop: 3 Sept 2001

    +++++++++++++++++++++++
    Submission Instructions
    +++++++++++++++++++++++

    We prefer papers to be submitted electronically in a postscript email
    attachment to both organizers simultaneously (i.e., to
    hilanb@cs.bris.ac.uk and korb@csse.monash.edu.au). Only if strictly
    necessary, submissions may be sent alternatively as an MS Word
    attachment. A last resort would be to mail or fax submissions to the
    address below.

            MLEPS Workshop
            c/o Kevin B. Korb
            School of Computer Science
            Monash University
            Clayton, VIC 3800
            AUSTRALIA

            Fax: +61 (03) 9905-5146

    ++++++++++++++++++++
    Workshop Organizers:
    ++++++++++++++++++++

    Hilan Bensusan (University of Bristol) hilanb@cs.bris.ac.uk
    Kevin Korb (Monash University) korb@csse.monash.edu.au

    ++++++++++++++++++
    Program Committee:
    ++++++++++++++++++

    Atocha Aliseda (Mexico)
    Hilan Bensusan (Bristol)
    Peter Flach (Bristol)
    Ronald Giere (Minn)
    Holger Hoos (UBC)
    Colin Howson (LSE)
    John Josephson (Ohio State)
    Kevin Kelly (CMU)
    Kevin Korb (Monash)
    Henry Kyburg (Rochester)
    David Pearce (DFKI)
    Peter Slezak (UNSW)
    Thomas Stuetzle (Darmstadt)
    Paul Thagard (UWO)
    Charles Twardy (Monash)
    Henry Tirri (Helsinki)
    Chris Wallace (Monash)
    Jon Williamson (KCL)
    Jan Zytkow (deceased)



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