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[资源] 【分享】Foundations of Knowledge Acquisition_Machine Learning.1993

Foundations of Knowledge Acquisition_Machine Learning
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Authors(Editors):
      Alan L. Meyrowitz
      Naval Research Laboratory
      Susan Chipman
      Office of Naval Research
      eds
Publisher: Kluwer Academic
Pub Date: 1993
Pages: 341
ISBN:
ISBN 0-7923-9277-9 (v. 1)
ISBN 0-7923-9278-7 (v. 2)

Foreword
One of the most intriguing questions about the new computer technology
that has appeared over the past few decades is whether we humans will
ever be able to make computers learn. As is painfully obvious to even
the most casual computer user, most current computers do not. Yet if
we could devise learning techniques that enable computers to routinely
improve their performance through experience, the impact would be
enormous. The result would be an explosion of new computer
applications that would suddenly become economically feasible (e.g.,
personalized computer assistants that automatically tune themselves to the
needs of individual users), and a dramatic improvement in the quality of
current computer applications (e.g., imagine an airline scheduling
program that improves its scheduling method based on analyzing past
delays). And while the potential economic impact of successful learning
methods is sufficient reason to invest in research into machine learning,
there is a second significant reason: studying machine learning helps us
understand our own human learning abilities and disabilities, leading to
the possibility of improved methods in education.
While many open questions remain about the methods by which machines
and humans might learn, significant progress has been made. For
example, learning systems have been demonstrated for tasks such as
learning how to drive a vehicle along a roadway (one has successfully
driven at 55 mph for 20 miles on a public highway), for learning to
evaluate financial loan applications (such systems are now in commercial
use), and for learning to recognize human speech (today's top speech
recognition systems all employ learning methods). At the same time, a
theoretical understanding of learning has begun to appear. For example,
we now can place theoretical bounds on the amount of training data a
learner must observe in order to reduce its risk of choosing an incorrect
hypothesis below some desired threshold. And an improved
understanding of human learning is beginning to emerge alongside our
improved understanding of machine learning. For example, we now
have models of how human novices learn to become experts at various
tasks ~ models that have been implemented as precise computer
programs, and that generate traces very much like those observed in
human protocols.
The book you are holding describes a variety of these new results. This
work has been pursued under research funding from the Office of Naval
Research (ONR) during the time that the editors of this book managed
an Accelerated Research Initiative in this area. While several
government and private organizations have been important in supporting
machine learning research, this ONR effort stands out in particular for
its farsighted vision in selecting research topics. During a period when
much funding for basic research was being rechanneled to shorter-term
development and demonstration projects, ONR had the vision to continue
its tradition of supporting research of fundamental long-range
significance. The results represent real progress on central problems of
machine learning. I encourage you to explore them for yourself in the
following chapters.
Tom Mitchell
Carnegie Mellon University

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