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.
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