From: kedar@ils.nwu.edu (Smadar Kedar)
Subject: IJCAI 93 Workshop on Machine Learning & K. Acquisition
Organization: The Institute for the Learning Sciences
Date: Fri, 25 Dec 1992 18:19:06 GMT


***********   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
(kedar@ils.nwu.edu)      (yk@lri.lri.fr)            (tecuci@aic.gmu.edu)

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: mlka93@aic.gmu.edu, fax: (703)993-3729


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