NetNews Usenet Archive 1992 #31/NN_1992_31.iso/spool/comp/ai/4698

Text File | 1992-12-25 | 6.9 KB | 164 lines

Newsgroups: comp.ai
Path: sparky!uunet!spool.mu.edu!uwm.edu!linac!mp.cs.niu.edu!news.ecn.bgu.edu!uxa.ecn.bgu.edu!news.ils.nwu.edu!kedar
From: [e-mail] (Smadar Kedar)
Subject: IJCAI 93 Workshop on Machine Learning and Knowledge Acquisi.
Message-ID: <[e-mail]>
Sender: [e-mail] (Mr. usenet)
Nntp-Posting-Host: aristotle.ils.nwu.edu
Organization: The Institute for the Learning Sciences
Date: Fri, 25 Dec 1992 18:14:10 GMT
Lines: 152


***********   CALL FOR PAPERS AND PARTICIPATION   ************

                        IJCAI-93 WORKSHOP
           MACHINE LEARNING AND KNOWLEDGE ACQUISITION:
Common Issues, Contrasting Methods, and Integrated Approaches

                     29 August 1993, Chambery, France

Machine learning and knowledge acquisition share the common goal
of acquiring and organizing the knowledge of a knowledge-based
system.  However, each field has a different focus, and most
research is still done in isolation from each other. The focus of
knowledge acquisition has been to improve and partially automate
the acquisition of knowledge from human experts. In contrast,
machine learning focuses on mostly autonomous algorithms for
acquiring or improving the organization of knowledge, often in
simple prototype domains. Also, in knowledge acquisition, the
acquired knowledge is directly validated by the expert that
expresses it, while in machine learning, the acquired knowledge
needs an experimental validation on data sets independent of those
on which learning took place. As machine learning moves to more
'real' domains, and knowledge acquisition attempts to automate
more of the acquisition process, the two fields increasingly find
themselves investigating common issues with complementary methods.  However, lack of common research methodologies, terminology, and underlying assumptions often hinder a close collaboration.  The purpose of this symposium is to bring together machine learning and knowledge acquisition researchers in order to
facilitate cross-fertilization and collaboration, and to promote
integrated approaches which could take advantage of the
complementary nature of machine learning and knowledge
acquisition.

Topics of interest include, but are not limited to, the following:

- Case Studies
  Case studies of integrated ML/KA methods, with analysis of
  successes/failures; integrated architectures for ML and KA;
  interactive learning systems, automated knowledge acquisition
  systems;

- Comparative Studies
  Comparative studies of KA and ML methods solving similar
  problems (e.g., knowledge base refinement methods in KA versus
  theory revision methods in ML, constructive induction in ML
  versus knowledge elicitation in KA). Analysis of the
  complementarity of the KA and ML approaches to knowledge base
  construction (e.g. KA primarily addresses the problems of KB
  elicitation and refinement, while ML primarily addresses issues
  of KB refinement and optimization).

- Hard Problems
  Analysis of hard problems in KA or ML that could be simplified
  by employing techniques from the other area, as well as
  presentation of specific solutions (e.g. the problem of new
  terms in ML could be simplified by employing knowledge
  elicitation techniques developed in KA; the credit/blame
  assignment problem in ML could be simplified by employing
  knowledge refinement techniques developed in KA; KA of problem
  solving rules could be automated by using apprenticeship
  learning techniques);

- Knowledge Representation
  Knowledge representation issues in KA and ML (adequate
  representations for KA, adequate representations for ML,
  approaches to knowledge representation in integrated ML/KA
  systems like translation between representations, common
  representations, etc.);

- Key Issues
  Key issues in ML or KA (e.g. dynamic versus static knowledge
  acquisition or learning, the role of explanations in KA and ML,
  the validation of knowledge in KA and ML);

- Overviews
  Overviews of the state-of-the-art of ML, KA or of the
  integration of ML and KA,

Position Papers
  Position papers on methodology for integrated ML/KA systems or
  on improving the collaboration between the ML and KA
  communities.

It is recommended that the papers make explicit the research
methodology, the underlying assumptions, definitions of technical
terms, important future issues, and potential points of
collaboration. They should not exceed 15 pages. The organizers
intend to publish a selection of the accepted papers as a book or
the special issue of a journal. They encourage the authors to take
this into account while preparing their papers.
The format of the workshop will be paper sessions with discussion
at the end of each session, and a concluding panel on the
integrated approaches, guidelines for successful collaboration,
and concrete action items. The number of the participants to the
workshop is limited to 40.

Each workshop attendee must also register for the IJCAI conference
and must pay an additional 300FF (about $60) fee for the workshop.
One student attending the workshop and being in charge of taking
notes will be exempted from the additional 300 FF fee. Volunteers
are invited.

SUBMISSIONS

Submit 4 copies of the papers (5 - 15 pages in length) to
arrive at the address for correspondence below, with the following schedule:

    Paper received:            March 31, 1993.
    Notice of acceptance:        May    10, 1993
    Final papers received:         June    10, 1993.

Those who would like to attend without a presentation should send
a one to two-page description of relevant research interests and a
list of selected publications.

WORKSHOP Co-CHAIRS

Smadar Kedar                Yves Kodratoff          Gheorghe Tecuci
NASA Ames & Inst.for   CNRS & Universite    George Mason Univ.&
the Learning Sciences    de Paris-Sud             Romanian Academy
([e-mail])      ([e-mail])            ([e-mail])

PROGRAM COMMITTEE

Ray Bareiss, Northwestern University
Catherine Baudin, NASA Ames
Guy Boy, Dialogics
Brian Gaines, University of Calgary
Matjas Gams, Jozef Stefan Institute
Jean-Gabriel Ganacia, Univ. Pierre and Marie Curie
Nathalie Mathe, European Space Agency and NASA Ames
Ryszard Michalski, George Mason University
Ray Mooney, University of Texas at Austin
Katharina Morik, Dortmund University
Mark Musen, Stanford University
Michael Pazzani, Univ. of California at Irvine
Luc De Raedt, Catholic University of Leuven
Alan Schultz, Naval Research Laboratory
Mildred Shaw, University of Calgary
Maarten van Someren, University of Amsterdam
Walter Van de Velde, University of Brussels

ADDRESS FOR CORRESPONDENCE

Gheorghe Tecuci
Artificial Intelligence Center, Computer Science Department
George Mason University, 4400 University Dr., Fairfax, VA 22030
email: [e-mail], fax: [phone]


--
=============================================================================

Dr. Smadar Kedar    [e-mail]