Report Number: CS-TR-82-950
Institution: Stanford University, Department of Computer Science
Title: Learning physical description from functional definitions,
examples and precedents
Author: Winston, Patrick H.
Author: Binford, Thomas O.
Author: Katz, Boris
Author: Lowry, Michael
Date: January 1983
Abstract: It is too hard to tell vision systems what things look like.
It is easier to talk about purpose and what things are for.
Consequently, we want vision systems to use functional
descriptions to identify things, when necessary, and we want
them to learn physical descriptions for themselves, when
This paper describes a theory that explains how to make such
systems work. The theory is a synthesis of two sets of ideas:
ideas about learning from precedents and exercises developed
at MIT and ideas about physical description developed at
Stanford. The strength of the synthesis is illustrated by way
of representative experiments. All of these experiments have
been performed with an implementation system.