CHIN%PLU@IO.ARC.NASA.GOV (05/07/88)
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National Aeronautics and Space Administration
Ames Research Center
SEMINAR ANNOUNCEMENT
SPEAKER: Smadar Kedar-Cabelli
Rutger University
TOPIC: Formulating Concepts and Analogies According to Purpose
ABSTRACT:
This talk describes research within the {\it explanation-based
generalization} (EBG) framework, a framework for producing deductive
generalizations from single examples. Despite recent progress, EBG
methods exhibit an important limitation: they are incapable of
determining which concepts are useful ones to acquire. More robust
generalizers must be able to automatically determine which concepts to
acquire based on the {\it purpose} of the learning, since concepts
acquired for one purpose may not be appropriate for another.
Our notion of the purpose of the learning is to acquire concepts which
will benefit an associated performance system. Two open issues become
apparent once EBG is associated with a performance system: How can EBG
acquire target concepts and definitions appropriate for the
performance system? Further, could the acquired target concept
definitions be used to improve subsequent performance?
The research focuses and investigates these issues in the context of a
specific type of performance system -- a state-space planner. The
approach is to provide EBG with explicit knowledge of the planner and
specific planning task. The {\it purposive concept formulation} and
{\it purposive explanation replay} methods, respectively, provide
solutions to the open problems.
We describe the prototype systems (PurForm and REPeat) which provide
experimental support for these methods. The results confirm that a
learning system {\it can} formulate concepts and analogies sensitive
to the purpose of the learning in restricted planning situations. We
discuss further extensions suggested by these results.
BIOGRAPHY:
Smadar Kedar-Cabelli has recently received her Ph.D. from the
Department of Computer Science at Rutgers University. She is
currently a research assistant at Rutgers, and a consultant for the
Learning Systems Group at Siemens Research and Technology Laboratories
in Princeton. Her research in machine learning focuses on open
problems within the explanation-based generalization (EBG) framework.
She has published a number of recent papers describing the
dissertation results. A paper presented at the 1987 National
Conference for Artificial Intelligence describes results on
formulating concept according to purpose. A paper describing the
close relationship of EBG and resolution-theorem proving was presented
at the Fourth International Machine Learning Workshop in 1987.
Earlier papers include a journal paper in Machine Learning, published
jointly with Mitchell and Keller, introducing the explanation-based
generalization framework.
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DATE: Thursday TIME: 2:00 - 3:00 pm BLDG. 244 Room 209
May 19, 1988 --------------
POINT OF CONTACT: Marlene Chin PHONE NUMBER: (415) 694-6525
NET ADDRESS: chin%plu@ames-io.arpa
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