MSIMS@RUTGERS.ARPA (03/26/84)
From: Michael Sims <MSIMS@RUTGERS.ARPA>
[Forwarded from the Rutgers bboard by Laws@SRI-AI.]
MACHINE LEARNING BROWN BAG SEMINAR
Speaker: Richard Keller
Date: Wednesday, March 28, 1984 - 12:00-1:30
Location: Hill Center, Room 254
Placing Learning in Context:
SOURCES OF CONTEXTUAL KNOWLEDGE FOR CONCEPT LEARNING
(Alternatively titled: The Mysterious Origins of LEX's Learning Goal)
In this talk, I will describe a new source of knowledge for concept
learning: knowledge of the learning context. Most previous research
in machine learning has failed to recognize contextual knowledge as a
distinct and useful form of learning knowledge. Contextual knowledge
includes, among other things, knowledge of the purpose for learning
and knowledge of the performance task to be improved by learning. The
addition of this meta-knowledge, which describes the learning process,
provides a broader perspective on learning than has been available to
most previous learning systems.
In general, learning systems that omit contextual knowledge have an
insufficient vantagepoint from which to supervise learning activity.
Both AM [Lenat-79] and LEX [Mitchell-83], for instance, were limited
by an inability to adapt to changes in their respective learning
environments, even when the changes were a result of their own
learning behavior. This limitation is not particularly surprising;
neither of these systems contained an explicit representation of the
task they were performing (specifically, mathematical discovery and
integral calculus problem solving, respectively). Nor did these
systems contain any knowledge about the relationship between learning
and the task performance. Before it is reasonable to expect a
learning system to adapt to changes in the task environment, it is
necessary to represent task knowledge and to incorporate this
knowledge into learning procedures. My research, therefore, focuses
on the representation and use of contextual knowledge -- including
task knowledge -- as guidance for concept learning.
In this talk, I will describe a learning framework that incorporates
the use of contextual knowledge. In addition, I will introduce
various alternative methods of representing contextual knowledge, and
sketch the design of some learning algorithms that utilize contextual
knowledge. Examples will be drawn, in large part, from my work on
incorporating contextual knowledge within the LEX learning system.