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One of the currently most active research areas within Artificial
Intelligence is the field of Machine Learning. which involves the
study and development of computational models of learning
processes. A major goal of research in this field is to build
computers capable of improving their performance with practice and
of acquiring knowledge on their own. The intent of this book is to
provide a snapshot of this field through a broad. representative
set of easily assimilated short papers. As such. this book is
intended to complement the two volumes of Machine Learning: An
Artificial Intelligence Approach (Morgan-Kaufman Publishers). which
provide a smaller number of in-depth research papers. Each of the
77 papers in the present book summarizes a current research effort.
and provides references to longer expositions appearing elsewhere.
These papers cover a broad range of topics. including research on
analogy. conceptual clustering. explanation-based generalization.
incremental learning. inductive inference. learning apprentice
systems. machine discovery. theoretical models of learning. and
applications of machine learning methods. A subject index IS
provided to assist in locating research related to specific topics.
The majority of these papers were collected from the participants
at the Third International Machine Learning Workshop. held June
24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list
of research projects covered is not exhaustive. we believe that it
provides a representative sampling of the best ongoing work in the
field. and a unique perspective on where the field is and where it
is headed.
One of the currently most active research areas within Artificial
Intelligence is the field of Machine Learning. which involves the
study and development of computational models of learning
processes. A major goal of research in this field is to build
computers capable of improving their performance with practice and
of acquiring knowledge on their own. The intent of this book is to
provide a snapshot of this field through a broad. representative
set of easily assimilated short papers. As such. this book is
intended to complement the two volumes of Machine Learning: An
Artificial Intelligence Approach (Morgan-Kaufman Publishers). which
provide a smaller number of in-depth research papers. Each of the
77 papers in the present book summarizes a current research effort.
and provides references to longer expositions appearing elsewhere.
These papers cover a broad range of topics. including research on
analogy. conceptual clustering. explanation-based generalization.
incremental learning. inductive inference. learning apprentice
systems. machine discovery. theoretical models of learning. and
applications of machine learning methods. A subject index IS
provided to assist in locating research related to specific topics.
The majority of these papers were collected from the participants
at the Third International Machine Learning Workshop. held June
24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list
of research projects covered is not exhaustive. we believe that it
provides a representative sampling of the best ongoing work in the
field. and a unique perspective on where the field is and where it
is headed.
Machine Learning: An Artificial Intelligence Approach contains
tutorial overviews and research papers representative of trends in
the area of machine learning as viewed from an artificial
intelligence perspective. The book is organized into six parts.
Part I provides an overview of machine learning and explains why
machines should learn. Part II covers important issues affecting
the design of learning programs-particularly programs that learn
from examples. It also describes inductive learning systems. Part
III deals with learning by analogy, by experimentation, and from
experience. Parts IV and V discuss learning from observation and
discovery, and learning from instruction, respectively. Part VI
presents two studies on applied learning systems-one on the
recovery of valuable information via inductive inference; the other
on inducing models of simple algebraic skills from observed student
performance in the context of the Leeds Modeling System (LMS). This
book is intended for researchers in artificial intelligence,
computer science, and cognitive psychology; students in artificial
intelligence and related disciplines; and a diverse range of
readers, including computer scientists, robotics experts, knowledge
engineers, educators, philosophers, data analysts, psychologists,
and electronic engineers.
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