MULLEN@SUMEX-AIM.ARPA (01/25/84)
From: Juanita Mullen <MULLEN@SUMEX-AIM.ARPA>
[Reprinted from the Stanford SIGLUNCH distribution.]
Friday, January 27, 1984
Chemistry Gazebo, between Physical & Organic Chemistry
12:05
SPEAKER: Tom Dietterich, HPP
Stanford University
TOPIC: Learning with Constraints
In attempting to construct a program that can learn the semantics of
UNIX commands, several shortcomings of existing AI learning techniques
have been uncovered. Virtually all existing learning systems are
unable to (a) perform data interpretation in a principled way, (b)
form theories about systems that contain substantial amounts of state
information, (c) learn from partial data, and (d) learn in a highly
incremental fashion. This talk will describe these shortcomings and
present techniques for overcoming them. The basic approach is to
employ a vocabulary of constraints to represent partial knowledge and
to apply constraint-propagation techniques to draw inferences from
this partial knowledge. These techniques are being implemented in a
system called, EG, whose task is to learn the semantics of 13 UNIX
commands (ls, cp, mv, ln, rm, cd, pwd, chmod, umask, type, create,
mkdir, rmdir) by watching "over-the-shoulder" of a teacher.