1983 INTERNATIONAL MACHINE LEARNING WORKSHOP:
                          AN INFORMAL REPORT

                              Jack Mostow
                  USC Information Sciences Institute
                          4676 Admiralty Way
                       Marina del Rey, CA. 90291

                       Version of July 18, 1983

  [NOTE: This is a draft of a report to appear in the October 1983
SIGART.  I am circulating it at this time to get comments before
sending it in.  The report should give the flavor of the work
presented at the workshop, but is not intended to be formal, precise,
or complete.  With this understanding, please send corrections and
questions ASAP (before the end of July) to MOSTOW@USC-ISIF.  Thanks.
- Jack]

  The first invitational Machine Learning Workshop was held at C-MU
in the summer of 1980; selected papers were eventually published in
Machine Learning, edited by the conference organizers, Ryszard
Michalski, Jaime Carbonell, and Tom Mitchell.  The same winning team
has now brought us the 1983 International Machine Learning Workshop,
held June 21-23 in Allerton House, an English manor on a park-like
estate donated to the University of Illinois.  The Workshop featured
33 papers, two panel discussions, countless bull sessions, very
little sleep, and lots of fun.

  This totally subjective report tries to convey one participant's
impression of the event, together with a few random thoughts it
inspired.  I have classified the papers rather arbitrarily under the
topics of "Analogy," "Knowledge Transformation," and "Induction"
(broadly construed), but of course 33 independent research efforts
can hardly be expected to fall neatly into any simple classification
scheme.  The papers are discussed in semi-random order; I have tried
to put related papers next to each other.

    [The entire document is about 12 pages of printed text.
     I am abridging it here; interested readers may FTP the
     file <AILIST>V1N24.TXT from SRI-AI. -- KIL]

1. Analogy
     1.1. Lessons
2. Knowledge Transformation
     2.1. Lessons
3. Induction
     3.1. Inducing Rules
     3.2. Dealing with Noise
     3.3. Logic-based Work
     3.4. Cognitive Modelling
     3.5. Lessons
4. Panel Discussion:  Cognitive Modelling -- Why Bother?
5. Panel Discussion:  "Machine Learning -- Challenges of the 80's"

6. A Bit of Perspective
  No overview would be complete without a picture that tries to put
everything in perspective:

     -------------> generalizations ------------
    |                                           |
    |                                           |
INDUCTION                                  COMPILATION
(Knowledge Discovery)                   (Knowledge Transformation)
    |                                           |
    |                                           v
examples ----------- ANALOGY  --------> specialized solutions
                (Knowledge Transfer)

 Figure 6-1:   The Learning Triangle:  Induction, Analogy, Compilation

  Of course the distinction between these three forms of learning
breaks down under close examination.  For example, consider LEX2:
does it induce heuristics from examples, guided by its definition of
"heuristic," or does it compile that definition into special cases,
guided by examples?

7. Looking to the Future
  The 1983 International Workshop on Machine Learning felt like
history in the making.  What could be a more exciting endeavor than
getting machines to learn?  As we gathered for the official workshop
photograph, I thought of Pamela McCorduck's Machines Who Think, and
wondered if twenty years from now this gathering might not seem as
significant as some of those described there.  I felt privileged to
be part of it.

  In the meantime, there are lessons to be absorbed, and work to be
done....

  One lesson of the workshop is the importance of incremental
learning methods.  As one speaker observed, you can only learn things
you already almost know.  The most robust learning can be expected
from systems that improve their knowledge gradually, building on what
they have already learned, and using new data to repair deficiencies
and improve performance, whether it be in analogy [Burstein,
Carbonell], induction [Amarel, Dietterich & Buchanan, Holland,
Lebowitz, Mitchell], or knowledge transformation [Rosenbloom,
Anderson, Lenat].  This theme reflects the related idea of learning
and problem-solving as inherent parts of each other [Carbonell,
Mitchell, Rosenbloom].

  Of course not everyone saw things the way I do.  Here's Tom
Dietterich again: ``I was surprised that you summarized the workshop
in terms of an "incremental" theme.  I don't think incremental-ness
is particularly important--especially for expert system work.
Quinlan gets his noise tolerance by training on a whole batch of
examples at once.  I would have summarized the workshop by saying
that the key theme was the move away from syntax.  Hardly anyone
talked about "matching" and syntactic generalization.  The whole
concern was with the semantic justifications for some learned
concept: All of the analogy folks were doing this, as were Mitchell,
DeJong, and Dietterich and Buchanan.  The most interesting point that
was made, I thought, was Mitchell's point that we need to look at
cases where we can provide only partial justification for the
generalizations.  DeJong's "causal completeness" is too stringent a
requirement.''

  Second, the importance of making knowledge and goals explicit is
illustrated by the progress that can be made when a learner has
access to a description of what it is trying to acquire, whether it
is a criterion for the form of an inductive hypothesis [Michalski et
al] or a formal characterization of the kind of heuristic to be
learned for guiding a search [Mitchell et al].

  Third, as Doug Lenat pointed out, continued progress in learning
will require integrating multiple methods.  In particular, we need
ways to combine analytic and empirical techniques to escape from
their limitations when used alone.

  Finally, I think we can extrapolate from the experience of AI in
the '60's and '70's to set a useful direction for machine learning
research in the '80's.  Briefly, in AI the '60's taught us that
certain general methods exist and can produce some results, while the
'70's showed that large amounts of domain knowledge are required to
achieve powerful performance.  The same can be said for learning.  I
consider a primary goal of AI in the '80's, perhaps the primary goal,
to be the development of general techniques for exploiting domain
knowledge.  One such technique is the ability to learn, which itself
has proved to require large amounts of domain knowledge.  Whether we
approach this goal by building domain-specific learners (e.g.
MetaDendral) and then generalizing their methods (e.g. version space
induction), or by attempting to formulate general methods more
directly, we should keep in mind that a general and robust
intelligence will require the ability to learn from its experience
and apply its knowledge and methods to problems in a variety of
domains.

  A well-placed source has informed me that plans are already afoot
to produce a successor to the Machine Learning book, using the 1983
workshop papers and discussions as raw material.  In the meantime,
there is a small number of extra proceedings which can be acquired
(until they run out) for $27.88 ($25 + $2.88 postage in U.S., more
elsewhere), check payable to University of Illinois.  Order from

     June Wingler
     University of Illinois at Urbana-Champaign
     Department of Computer Science
     1304 W. Springfield Avenue
     Urbana, IL 61801