CALL FOR PAPER - Machine Learning in Engineering
Benoit Julien
CALL FOR PARTICIPATION
*** Workshop on Machine Learning in Engineering ***
International Joint Conference on Artificial Intelligence 1995
IJCAI-95
Montreal, Quebec, Canada
August 19-25, 1995
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WORKSHOP OBJECTIVES
The last ten years have witness a significant increase in the development of
knowledge-based systems for engineering applications. As in other domains,
the success of knowledge-based approaches in engineering depends critically
on the quality of the knowledge acquisition process. Computer-aided
engineering system developers in the early nineties quickly recognized the
potentials offered by emerging machine learning techniques.
As machine learning moves from "toy" problems to "real" engineering
applications, a concerted R&D effort becomes essential to identify and
overcome critical engineering knowledge acquisition bottlenecks. In that
perspective, this workshop will bring together researchers applying or
developing machine learning techniques for various engineering disciplines in
order to establish important commonalities and differences in engineering
learning problems. This forum will permit the definition of basic engineering
learning tasks and their relationships with appropriate machine learning
strategies. By presenting the state-of-the-art in machine learning
applications to engineering, this event should also bridge many gaps between
machine learning theory and engineering practice.
TOPICS OF INTEREST
All researchers and practitioners actively applying or developing machine
learning techniques to engineering problems are encouraged to submit papers
for this workshop. Topics of interest include, but are not limited to, the
following:
* Case studies
Case studies of application of machine learning in engineering,
with analysis of successes and failures. Examples of application
topics:
- Knowledge mining of engineering databases;
- Engineering learning apprentice systems;
- Semi-automated engineering knowledge acquisition;
- Constructive induction in engineering;
- Engineering knowledge discovery systems;
- Engineering model acquisition and refinement.
* Comparative studies
Comparative studies of machine learning techniques solving similar
engineering learning tasks;
* Overviews
Overviews of the state-of-the-art of machine learning in engineering;
* Position papers on key issues
Position papers discussing and proposing methodologies for solving
important engineering learning issues. Examples of key issues:
- Prior knowledge in engineering learning problems;
- Tracking engineering concept drifts (dynamic knowledge);
- Mapping of generic engineering tasks with learning techniques;
- Multistrategy learning for engineering problems;
- Machine learning for engineering data analysis;
- Learning from very small or very large training sets;
- Learning from noisy training sets;
- Integration of machine learning and knowledge acquisition.
Papers describing strictly manual knowledge acquisition and maintenance
case studies are discouraged. This workshop does not cover applications of
subsymbolic learning techniques such a neural networks and genetic algorithms.
SUBMISSIONS
All papers submitted should not exceeed 15 pages. The organizers intend to
publish a selection of the accepted papers as a book or a special issue of a
journal. The authors should take this into account while preparing their
papers. In order to encourage the submission of work in progress reports,
5 pages extended abstracts will also be accepted for submission. However,
the accepted extended abstracts will not be considered for later publication.
Copies of the workshop proceedings containing all accepted papers and
extended abstracts will be prepared and made available by IJCAI at the
workshop.
Each paper and extended abstract should provide a clear description of the
engineering task and the learning problem so that other participants not
familiar with the application can easily understands the key characteristics
and objective of the research. The papers should also define all technical
terms and make explicit the research methodology and the underlying
characteristics and assumptions of the learning problem(s) and technique(s).
The authors should also discuss important future issues as well as
implications and possible extensions of their work to other engineering
domains.
Each submitted paper and extended abstract will be reviewed by at least
three members of an international program committee and will be judged
on significance, originality, and clarity. Papers submitted simultaneously
to other conferences or journals must state so on the title page.
Those who would like to attend the workshop without giving a presentation
should send a 1 page description of relevant research interests with a short
list of selected publications.
Please send general inquiries to jul...@crim.ca.
DEADLINES
Four (4) hard copies of the papers or extended abstracts must be received
by the workshop organiser by February 17, 1995. Alternatively, electronic
submissions in postscript are encouraged. FAX submissions are not acceptable.
Notification of acceptance or rejection will be sent to the first
(or designated) author with the reviewers comments by March 24, 1995.
Final camera-ready papers and extended abstracts should arrive by
April 21, 1995. This one-day workshop will be held between Saturday
19 August and Monday 21 August 1995 inclusive.
PAPER FORMAT
Submissions must be clearly legible, with good quality print. Papers and
extended abstracts are respectively limited to a total of 15 and 5 pages
including title page, bibliography, tables and figures. Papers must be
printed on 8.5 x 11 inch paper or A4 paper using 12 point type (10 characters
per inch) with a 1 inch margins and no more than 40 lines per page. The
title page must include the names, postal and electronic (e-mail) addresses
and phone and FAX numbers of all authors together with an abstract (200 words
maximum) and a list of key words. The first key words should specify the
engineering domain (e.g., electrical, civil, mechanical, industrial,
chemical, environmental, metalurgy, mining), the engineering generic task
(e.g., classification, scheduling, control, maintenance, planning, design),
and the machine learning technique(s) used (e.g., case-based learning,
conceptual clustering, explanation-based learning, rule induction, inductive
predicate logic).
Papers without this format will not be reviewed. To save paper and postage
costs please use double-sided printing or, preferably, send a postcript file
via internet to the workshop organizer.
WORKSHOP FORMAT
The format of the workshop will be paper sessions with discussion at the end
of each session. The day will be divided in four (4) thematic sessions of
an hour and a half each. A commentator from the program committee will be
assigned for each presentation so as to initiate and supervised the
discussions. The workshop will conclude with a panel discussion. The panel
discussions will be instrumental in establishing guidelines for future
integrations and collaborations and a research agenda for the next five years
based on the key multidisciplinary issues identified.
The number of participants to the workshop is limited to 40. All workshop
participants are expected to register for the main IJCAI conference and to
pay an additional fee ($US 50) for the workshop.
WORKSHOP CHAIRS
Benoit Julien (workshop organiser)
Centre de recherche informatique de Montreal (CRIM)
1801, McGill College avenue, Suite 800
Montreal (Quebec) H3A 2N4
Canada
phone: 1-514-398-5862
fax: 1-514-398-1244
e-mail: jul...@crim.ca
Steven J. Fenves
Department of Civil Engineering
Carnegie Mellon University
Pittsburgh, PA, 15213
United States
phone: 1-412-268-2944
fax: 1-412-268-7813
e-mail: fen...@ce.cmu.edu
Tomasz Arciszewski
Systems Engineering Department
School of Information Technology and Engineering
George Mason University
Fairfax, VA, 22030
United States
phone: 1-703-993-1513
fax: 1-703-993-1706
e-mail: Tarc...@gmu.edu