By Topic

LEW: learning by watching

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
P. Constant ; ENST, Paris, France ; S. Matwin ; F. Oppacher

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

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:12 ,  Issue: 3 )