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Machine Learning List Vol. 6 No. 07
Machine Learning List: Vol. 6 No. 7
Wednesday, March 9, 1994
Contents:
AAAI 94 Workshop on Indexing and Reuse in Multimedia Systems
CFP: IEEE-SMC Special Issue on Learning Robots
AID'94 Workshop on Machine Learning in Design
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Date: Tue, 8 Mar 1994 16:42:25 -0600
From: kedar@ils.nwu.edu
Subject: AAAI 94 Workshop on Indexing and Reuse in Multimedia Systems
AAAI 94 Workshop on Indexing and Reuse in Multimedia Systems
Multimedia systems for documentation, training, entertainment, or news
use a variety of indexing schemes to annotate and access media for
further use. As these systems grow, issues of how to represent,
acquire, and refine indexing knowledge for effective information
access and reuse become crucial. Yet, analysis of the indexing task
and research on indexing methods and tools for multimedia are just
beginning to emerge.
Because automated parsing techniques for time-based and visual media
are currently in the early stages of development, systems for
multimedia material must often operate with partial representations of
their information content. However, these systems can still produce
information that is useful for the end-user. Such hybrid human-machine
representation and communication provides opportunities to investigate
interactive methods for index acquisition and refinement, which may
complement and improve automatic parsing techniques.
This workshop focuses on methods and tools for indexing, searching,
and reusing information in multimedia systems. We would like to bring
together researchers in knowledge acquisition, case-based reasoning,
multimedia system design, human-computer interaction, speech/visual
perception, and other relevant disciplines. We also encourage
contributions from video archivists, videographers, editors, or sound
designers who may not have an AI background, but have experience in
representing and indexing multiple media.
We welcome contributions in the areas of: (1) indexing tools; (2)
index representations and refinement for information reuse; and (3)
studies of indexing practice. Specifically:
INDEXING TOOLS:
o Knowledge acquisition or machine learning techniques to acquire or
refine indexing knowledge. Incremental refinement of semiformal
representations. Incremental generation of hypermedia links, etc.
o Integration of automated parsing techniques that process signals or
text with conceptual (semantic) indexing techniques.
o Analysis of the types of interactions with humans that a system can
use to reorganize its memory.
INDEX REPRESENTATIONS AND REFINEMENT FOR REUSE:
o Index representations and reuse for different purposes/classes of
users.
o Completing, generalizing, adding context to indexing schemes.
o Index representations and acquisition which address the media-specific
properties of time-based and visual media.
o Comparison between different indexing schemes: domain-dependent vs.
domain-independent, semantic vs. episodic, absolute (information
content) vs. relative (links between information fragments as in
hypermedia).
o Empirical evaluation of information organization and reuse in
multimedia systems.
THE PRACTICE OF INDEXING:
o Case studies and analysis of indexing practice which deal with
pragmatic questions: Who are the indexers? How deep must their
understanding be of the content of the indexed information? When is
indexing performed, in real-time during the data generation or after
the fact?
The duration of the workshop will be one day and will consist of at
most fifty (50) participants. To leave time for discussion only a
subset of the selected papers will be presented. Each session will
comprise: (1) presentations by authors of selected papers; (2) a short
analysis/critique of the papers presented by the session chair; (3) a
discussion with the authors and the workshop participants. In
addition we will have a panel and one or two invited speakers.
Workshop committee:
Catherine Baudin (chair)
AI Research Branch
NASA Ames Research Center. MS 269/2
Moffett Field, CA 94035. USA.
Tel (415) 604 4745
FAX (415) 604 3594
baudin@ptolemy.arc.nasa.gov
Smadar Kedar, ILS, Northwestern University, kedar@ils.nwu.edu
(co-chair) Daniel M. Russell, Apple computer inc.,
dmrussel@taurus.apple.com (co-chair) Marc Davis, MIT Media Laboratory
and Interval Research Corp., davis@interval.com (co-chair)
Ray Bareiss, ILS, Northwestern University,
Guy Boy, EURISCO,
Tom Gruber, Stanford University,
Ken Haase, MIT Media Lab,
Doug Lenat, MCC,
Nathalie Mathe, NASA Ames Research Center
Scott Minneman, Xerox PARC
Dick Osgood, ILS, Northwestern University
Jim Spohrer, Apple Computer Inc.
Meg Withgott, Interval Research Corp.
Please send four copies of a five to twelve page paper to Catherine
Baudin. The papers should be received by March 18. Those who would like
to attend without submitting a paper should send a one to two page
description of their relevant research interests. Presenters are
encouraged to bring live demos and/or videos of their work. Organizers
intend to publish a selection of the accepted papers as a book or as a
special issue of a journal.
------------------------------
Date: 9 Mar 1994 10:25:16 -0600
From: mdorigo@ulb.ac.be (Marco Dorigo)
Subject: CFP: IEEE-SMC Special Issue on Learning Robots
Call For Papers:
Special issue of IEEE Transactions on Systems, Man and Cybernetics
(IEEE-SMC) on:
Learning Approaches to Autonomous Robots Control.
Guest Editor: Marco Dorigo
Submission deadline: May 20, 1994.
Recent research on control of autonomous robots (or agents) has
increasingly focused on the development and application of new learning
paradigms. This issue of robot control has been addressed in a number of
research areas including the following:
- Reinforcement learning (Q-learning, Classifier systems, etc.).
- Evolutionary Computation.
- Evolving neural nets.
- Neurocontrol and neurodynamics.
- Adaptive fuzzy systems.
- Artificial life.
The aim of the special issue of IEEE-SMC is to draw together current
research on a variety of these learning techniques (used and developed in
some or all of the above research fields) which have been applied to real
robots' control, as well as to discuss the implications this research has
on the design and development of robots in general. This includes the
following (not exhaustive) sub-topics:
- Learning approaches to stimulus-response robots.
- Learning approaches to robots acting in real environments.
- Supervised (that is, with a trainer) and unsupervised type of behavior
learning.
- Hierarchical architectures for learning robots.
- Trade-offs between learning and design.
- Interplay between reactive and reasoning type of robot activity in a
"learning perspective."
- Robustness of learning techniques to noisy environments and to
unreliable
sensors and/or actuators.
- Ethologically inspired learning architectures for autonomous robots.
- Foundational analysis of interdependence among situated activity,
learning algorithms, and degree of environmental complexity.
- Cooperative learning robots.
Papers on research still in its "simulation" phase, that is, yet to be
implemented on real autonomous robots, will also be considered if it has
clear and relevant implications for still to come concrete realization.
To be considered for the special issue, five copies of each paper must be
received by the editor at the address below by May 20, 1994. The first
page
should include a descriptive title, the names and addresses of all
authors,
a brief abstract, and salient keywords. Submissions will be carefully
refereed for technical contribution, originality of the approach,
practical
significance, and clarity of presentation (according to the standard IEEE
Transactions criteria), as well as salience to the topic of the special
issue.
Notifications will be sent by September 15, 1994, and final versions of
accepted papers will be due two months later.
Expected publication is mid-1995.
Marco Dorigo (Editor)
IRIDIA
Universite' Libre de Bruxelles
Avenue Franklin Roosvelt 50
CP 194/6 1050 Bruxelles
Belgium
tel. +32-2-6503167
fax +32-2-6502715
mdorigo@ulb.ac.be.
All prospective contributors should get in touch with the editor as soon
as
possible, and in any case well before the submission deadline, in order to
receive more detailed information on the sort of research that the
IEEE-SMC
special issue is expected to cover. Such responses will also help us with
the organization of reviews, and with last minute communications (such as
change of Editor's address).
Queries on any aspect of the above should also be directed to the above
address.
------------------------------
From: Alex Duffy <alex@cad.strath.ac.uk>
Date: Wed, 9 Mar 94 11:31:51 GMT
Subject: AID'94 Workshop on Machine Learning in Design
MACHINE LEARNING IN DESIGN
==========================
workshop to be held prior to the
THIRD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN DESIGN
Swiss Federal Institute of Technology, Lausanne, Switzerland
15-18 August 1994
Design Learning vs Machine Learning
Overview
The Machine Learning (ML) in Design workshop is part of the Artificial
Intelligence in Design '94 conference. This workshop is intended
to provide a forum for provocative discussion related to the
application and evolution of machine learning techniques in design.
To improve a product's quality or reduce the design cycle time
designers require the right information, at the right time.
Developments in computing technology present designers with ever
increasing amounts of information and improved access. Therefore,
the effective and efficient re-use of past design procedures and
product knowledge is becoming more critical in contemporary design.
Designers not only use information of specific experiences but also,
by learning and understanding salient issues, they can abstract or
generalise knowledge from those experiences. For example, they learn
how to carry out design, what key decisions need to be made and when,
what factors are crucial to decisions and what are the implications
of those decisions, about the product itself, it's life expectancy
and environment, it's manufacturability, the current state of
technology, trends in the market place, and a lot more besides.
Designers use their learned knowledge to create new designs (which
may be novel or innovative) in an attempt to produce not only feasible
but competitive products. Consequently, there is a growing realisation
that for future computer-based design systems to be more effective they
must continually evolve their state of knowledge to reflect new
experiences and that they must use that knowledge in all aspects of
design problem solving.
By supporting the learning process it can be argued that computer based
systems can become more effective tools which are better equipped to
aid designers make well-informed decisions. Machine Learning, whose
utility has been explored typically in chemistry, game-playing, image
recognition, and many other fields, provides a basis to capitalise on
the utility of inherent and explicit past design knowledge. However
the application of machine learning in design is relatively immature
and as such presents exciting and challenging issues to Machine
Learning and Intelligent CAD researchers alike.
=======================================================================
Purpose of the workshop
The purpose of the workshop is to explore the issues and requirements
of learning in design with a view of critically evaluating the current
and required support from machine learning techniques. The objective
is not only to identify key areas for future research but also to
stimulate synergy in the Machine Learning in Design research community.
=======================================================================
Workshop Format and Topics
The workshop will run for half a day prior to the main AI in Design
conference. Its format will depend upon submitted position papers but
is likely to consist of a number of small working groups tackling
identified issues and presenting their deliberations to the workshop
participants for discussion. Topics may address issues such as: How
can machine learning techniques be adapted or extended to support learning
in design? What are the limitations of ML in Design ? Why does design
present distinct challenges to ML research ? etc.
=======================================================================
Guidelines for Position Papers
Position papers (in ascii or postscript form) of around three to four
pages should be submitted by email to alex@cad.strath.ac.uk (Alex Duffy)
no later than the 2nd June 1994.
All accepted position papers will be distributed to the participants.
=======================================================================
Number of Participants
To stimulate lively debate and constructive discussions numbers will
be restricted to around 20-30 participants. Admission, as determined
by a selected panel, will be based upon the expediency of the submitted
position papers and limited to attendees of the AI in Design conference.
Each participant will be charged a fee of SFr 75 to cover costs of
workshop notes, administration and refreshments.
=======================================================================
Co-ordinators/Organisers
Convenor:
Dr Alex H B Duffy, University of Strathclyde, alex@cad.strath.ac.uk
Co-Convenors:
Prof David C Brown, Worcester Polytechnic Institute, dcb@willow.wpi.edu
Prof Mary Lou Maher, University of Sydney, mary@archsci.arch.su.EDU.AU
International Advisory Panel:
Dr Tomasz Arciszewski (USA)
Prof Ivan Bratko (Slovenia)
Dr Ashok Goel (USA)
Dr Yoram Reich (Israel)
Prof Derek Sleeman (UK)
=======================================================================
Further information
If you require further information then please contact:
Dr Alex Duffy Postal Address: CAD Centre
JANET: alex@cad.strath.ac.uk University of Strathclyde
Phone:+44-41-552-4400 Ext. 3005 75 Montrose Street
Fax :+44-41-552-3148 Glasgow G1 1XJ
Scotland, UK.
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End of ML-LIST (Digest format)
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