KEDAR-CABELLI@RED.RUTGERS.EDU (Smadar) (11/09/86)
Reminder: Dissertation Defense for Rich Keller
Time and Place: Thursday, Nov. 13, 1:30 p.m., Hill 423
Committee: Tom Mitchell (chair)
Thorne McCarty
Lou Steinberg
Jack Mostow
Abstract:
The Role of Explicit Contextual Knowledge in
Learning Concepts to Improve Performance
Richard M. Keller
(KELLER@RED.RUTGERS.EDU)
This dissertation addresses some of the difficulties encountered
when using artificial intelligence-based, inductive concept learning
methods to improve an existing system's performance. The underlying
problem is that inductive methods are insensitive to changes in the
system being improved by learning. This insensitivity is due to the
manner in which contextual knowledge is represented in an inductive
system. Contextual knowledge consists of knowledge about the context
in which concept learning takes place, including knowledge about the
desired form and content of concept descriptions to be learned (target
concept knowledge), and knowledge about the system to be improved by
learning and the type of improvement desired (performance system
knowledge). A considerable amount of contextual knowledge is
"compiled" by an inductive system's designers into its data structures
and procedures. Unfortunately, in this compiled form, it is difficult
for the learning system to modify its contextual knowledge to
accommodate changes in the learning context over time.
This research investigates the advantages of making contextual
knowledge explicit in a concept learning system by representing that
knowledge directly, in terms of express declarative structures. The
thesis of this research is that aside from facilitating adaptation to
change, explicit contextual knowledge is useful in addressing two
additional problems with inductive systems. First, most inductive
systems are unable to learn approximate concept descriptions, even
when approximation is necessary or desirable to improve performance.
Second, the capability of a learning system to generate its own
concept learning tasks appears to be outside the scope of current
inductive systems.
To investigate the thesis, this study introduces an alternative
concept learning framework -- the concept operationalization framework
-- that requires various types of contextual knowledge as explicit
inputs. To test this new framework, an existing inductive concept
learning system (the LEX system [Mitchell et al. 81]) was rewritten as
a concept operationalization system (the MetaLEX system). This
dissertation describes the design of MetaLEX and reports results of
several experiments performed to test the system. Results confirm the
utility of explicit contextual knowledge, and suggest possible
improvements in the representations and methods used by the system.
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