LEW: learning by watching
Constant, P.
Matwin, S.
Oppacher, F.
ENST, Paris;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Mar 1990
Volume: 12,
Issue: 3
On page(s): 294-308
ISSN: 0162-8828
References Cited: 33
CODEN: ITPIDJ
INSPEC Accession Number: 3641641
Digital Object Identifier: 10.1109/34.49054
Current Version Published: 2002-08-06
Abstract
LEW (learning by watching), a machine learning system, is
described. It was designed for knowledge acquisition in cooperation with
an expert. LEW learns from examples of problem-solution (or
question-answer) pairs by generalizing on differences in those pairs. In
this sense, it belongs to the family of inductive learning methods. It
provides for using background knowledge through the environment
component of problem-solution pairs, thereby making constructive
learning possible. The user can control the extent of the
generalizations performed by LEW. The learning method is incremental
and, to some extent, noise-resistant. The authors give an informal
overview of the knowledge representation and the basic learning
algorithm of LEW and indicate that the system's design meets the stated
criteria and enables it to give helpful assistance, even in situations
characterized by noisy or conflicting information and by lack of
extensive background knowledge. The theory behind LEW is presented,
along with rigorous definitions of its fundamental concepts and a
general description of its learning algorithm. LEW's functioning with
some larger examples, one from the QUIZ Advisor domain and another from
the domain of block-world planning, is illustrated. The authors compare
LEW with several other knowledge acquisition tools and introduce a
precise characterization of learning from near misses and a near-miss
metric. Possible extensions and enhancements to the system are noted
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