THE OTHER SIDE OF THE COIN: TEACHING ARTIFICIAL LEARNING SYSTEMS
Date
1988-09-01
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
The burgeoning technology of machine learning is beginning to provide some
insight into the nature of learning and the role of teaching in expediting
the learning process. A number of systems that learn concepts and procedures
from examples have been described in the research literature. In general
these require a teacher who not only has an analytical understanding of
the problem domain, but also is familiar with some of the internal workings
of the learning system itself. This is because the learner is performing
a search in concept space which is generally quite intractable, but for
the teacher's selection of guiding examples.
A concept learning system's teacher must select a complete, properly
ordered set of examples-one that results in a successful search by the
system for an appropriate concept description. In some systems the set
of examples determines whether the concept can or cannot be learned,
while the order of presentation affects execution time alone. In others,
both examples and presentation order are jointly responsible for success.
Yet others occasionally select critical examples themselves and present
them to a teacher for classification. In all cases, however, the teacher
provides the primary means whereby search is pruned. Sometimes the teacher
must prime the learner with considerable initial knowledge before
learning can begin.
Not surprisingly, systems which demand more of the teacher are able to
learn more sophisticated concepts. This paper examines the relationship
between teaching requirements and learning power for current concept
learning systems. We introduce concept learning by machine with
emphasis on the role of the human teacher in rendering practical an
otherwise intractable concept search. Machine learning has drawn many
lessons from human learning and will continue to do so. In turn it can
contribute more formal, if simpler, analysis of concept learning from
examples.
Description
Keywords
Computer Science