CFP Constructive Induction Workshop, Due March 1st

[charles anderson]

			   CALL FOR PAPERS
		    1991 MACHINE LEARNING WORKSHOP
	     Northwestern University    June 27-29, 1991
				   
			CONSTRUCTIVE INDUCTION

    Selection of an appropriate representation is critical to the
success of most learning systems.  In difficult learning problems
(e.g., protein folding, word pronunciation, relation learning),
considerable human effort is often required to identify the basic
terms of the representation language.  Constructive induction offers a
partial solution to this problem by automatically introducing new
terms into the representation as needed.  Automatically constructing
new terms is difficult because the environment or teacher usually
provides only indirect feedback, thus raising the issue of credit
assignment.  However, as learning systems face tasks of
greater autonomy and complexity, effective methods for constructive
induction are becoming increasingly important.

    The objective of this workshop is to provide a forum for the
interchange of ideas among researchers actively working on constructive
induction issues.  It is intended to identify commonalities and
differences among various existing and emerging approaches such as
knowledge-based term construction, relation learning, theory revision
in analytic systems, learning of hidden-units in multi-layer neural
networks, rule-creation in classifier systems, inverse resolution, and
qualitative-law discovery.

    Submissions are encouraged in the following topic areas:

      o Empirical approaches and the use of inductive biases

      o Use of domain knowledge in the construction and evaluation of
        new terms

      o Construction of or from relational predicates

      o Theory revision in analytic-learning systems

      o Unsupervised learning and credit assignment in constructive
        induction

      o Interpreting hidden units as constructed features

      o Constructive induction in human learning

      o Techniques for handling noise and uncertainty

      o Experimental studies of constructive induction systems

      o Theoretical proofs, frameworks, and comparative analyses

      o Comparison of techniques from empirical learning, analytical
        learning, classifier systems, and neural networks


    Send six copies of paper submissions (4000 word maximum) to
Christopher Matheus, GTE Laboratories, 40 Sylvan Road, MS-45, Waltham
MA 02254 (matheus at gte.com).  Submissions must be received by March 1,
1991.  Include a cover page with authors' names, addresses, phone
numbers, electronic mail addresses, paper title, and a 300 (maximum)
word abstract.  Do not indicate or allude to authorship anywhere
within the paper.  Acceptance notification will be mailed by April 30,
1991.  Accepted papers will be allotted four two-column pages for
publication in the Proceedings of the 1991 Machine Learning Workshop.



Organizing Committee:                   Program Committee:

Christopher Matheus, GTE Laboratories   Chuck Anderson, Colorado State
George Drastal, Siemens Corp. Research  Gunar Liepins, Oak Ridge National Lab.
Larry Rendell, University of Illinois   Douglas Medin, University of Michigan
                                        Paul Utgoff, University of Massachusetts