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Machine Learning List Vol. 3 No. 01
Machine Learning List: Vol. 3 No. 1
Tuesday, Jan 15, 1991
Contents:
ML Workshop
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Date: Tue, 8 Jan 91 14:54:23 CST
From: Lawrence Birnbaum <birnbaum@fido.ils.nwu.EDU>
Subject: Machine learning workshop: Addendum to call for papers
ADDENDUM TO CALL FOR PAPERS
EIGHTH INTERNATIONAL WORKSHOP ON MACHINE LEARNING
NORTHWESTERN UNIVERSITY
EVANSTON, ILLINOIS
JUNE 27-29, 1991
We wish to clarify the position of ML91 with respect to the issue of multiple
publication. In accordance with the consensus expressed at the business
meeting at ML90 in Austin, ML91 is considered by its organizers to be a
specialized workshop, and thus papers published in its proceedings may overlap
substantially with papers published elsewhere, for instance IJCAI or AAAI.
The sole exception is with regard to publication in future Machine Learning
Conferences. Authors who are concerned by this constraint will be given the
option of foregoing publication of their presentation in the ML91 Proceedings.
The call for papers contained information concerning seven of the eight
individual workshops that will make up ML91. Information concerning the final
workshop follows.
Larry Birnbaum
Gregg Collins
Northwestern University
The Institute for the Learning Sciences
1890 Maple Avenue
Evanston, IL 60201
(708) 491-3500
[Now that the issue of submission to multiple conferences is settled, do we
need a policy for an author submitting the same paper to two (or more)
workshops at MLW? How about a single author submitting different papers
to different workshops? Clearly, an author cannot fully participate
in both -MP].
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COMPUTATIONAL MODELS OF HUMAN LEARNING
This workshop will foster interaction between researchers concerned with
psychological models of learning and those concerned with learning systems
developed from a machine learning perspective.
We see several ways in which simulations intended to model human learning and
algorithms intended to optimize machine learning may be mutually relevant.
For example, the way humans learn and the optimal method may turn out to be
the same for some tasks. On the other hand, the relation may be more
indirect: modeling human behavior may provide task definitions or constraints
that are helpful in developing machine learning algorithms; or machine
learning algorithms designed for efficiency may mimic human behavior in
interesting ways.
We invite papers that report on learning algorithms that model or are
motivated by learning in humans or animals. We encourage submissions that
address any of a variety of learning tasks, including category learning, skill
acquisition, learning to plan, and analogical reasoning. In addition, we hope
to draw work from a variety of theoretical approaches to learning, including
explanation-based learning, empirical learning, connectionist approaches, and
genetic algorithims.
In all cases, authors should explicitly identify 1) in what ways the system's
behavior models human (or animal) behavior, 2) what principles in the
algorithm are responsible for this, and 3) the methods for comparing the
system's behavior to human behavior and for evaluating the algorithm. A
variety of methods have been proposed for computational psychological models;
we hope the workshop will lead to a clearer understanding of their relative
merits. Progress reports on research projects still in development are
appropriate to submit, although more weight will be given to projects that
have been implemented and evaluated. Integrative papers providing an analysis
of multiple systems or several key issues are also invited.
WORKSHOP COMMITTEE
Dorrit Billman (Georgia Tech)
Randolph Jones (Univ. of Pittsburgh)
Michael Pazzani (Univ. of California, Irvine)
Jordan Pollack (Ohio State Univ.)
Paul Rosenbloom (USC/ISI)
Jeff Shrager (Xerox PARC)
Richard Sutton (GTE)
SUBMISSION DETAILS
Papers should be approximately 4000 words in length. Authors should submit
seven copies, by March 1, 1991, to:
Dorrit Billman
School of Psychology
Georgia Institute of Technology
Atlanta, GA 30332
phone (404) 894-2349
Formats and deadlines for camera-ready copy will be communicated upon
acceptance.
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END of ML-LIST 3.1
