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Based on a symposium honoring the extensive work of Allen Newell --
one of the founders of artificial intelligence, cognitive science,
human-computer interaction, and the systematic study of
computational architectures -- this volume demonstrates how
unifying themes may be found in the diversity that characterizes
current research on computers and cognition. The subject matter
includes:
* an overview of cognitive and computer science by leading
researchers in the field;
* a comprehensive description of Allen Newell's "Soar" -- a
computational architecture he developed as a unified theory of
cognition;
* commentary on how the Soar theory of cognition relates to
important issues in cognitive and computer science;
* rigorous treatments of controversial issues in cognition --
methodology of cognitive science, hybrid approaches to machine
learning, word-sense disambiguation in understanding material
language, and the role of capability processing constraints in
architectural theory;
* comprehensive and systematic methods for studying architectural
evolution in both hardware and software;
* a thorough discussion of the use of analytic models in human
computer interaction;
* extensive reviews of important experiments in the study of
scientific discovery and deduction; and
* an updated analysis of the role of symbols in information
processing by Herbert Simon.
Incorporating the research of top scientists inspired by Newell's
work, this volume will be of strong interest to a large variety of
scientific communities including psychologists, computational
linguists, computer scientists and engineers, and interface
designers. It will also be valuable to those who study the
scientific process itself, as it chronicles the impact of Newell's
approach to research, simultaneously delving into each scientific
discipline and producing results that transcend the boundaries of
those disciplines.
Based on a symposium honoring the extensive work of Allen Newell --
one of the founders of artificial intelligence, cognitive science,
human-computer interaction, and the systematic study of
computational architectures -- this volume demonstrates how
unifying themes may be found in the diversity that characterizes
current research on computers and cognition. The subject matter
includes:
* an overview of cognitive and computer science by leading
researchers in the field;
* a comprehensive description of Allen Newell's "Soar" -- a
computational architecture he developed as a unified theory of
cognition;
* commentary on how the Soar theory of cognition relates to
important issues in cognitive and computer science;
* rigorous treatments of controversial issues in cognition --
methodology of cognitive science, hybrid approaches to machine
learning, word-sense disambiguation in understanding material
language, and the role of capability processing constraints in
architectural theory;
* comprehensive and systematic methods for studying architectural
evolution in both hardware and software;
* a thorough discussion of the use of analytic models in human
computer interaction;
* extensive reviews of important experiments in the study of
scientific discovery and deduction; and
* an updated analysis of the role of symbols in information
processing by Herbert Simon.
Incorporating the research of top scientists inspired by Newell's
work, this volume will be of strong interest to a large variety of
scientific communities including psychologists, computational
linguists, computer scientists and engineers, and interface
designers. It will also be valuable to those who study the
scientific process itself, as it chronicles the impact of Newell's
approach to research, simultaneously delving into each scientific
discipline and producing results that transcend the boundaries of
those disciplines.
Recent Advances in Robot Learning contains seven papers on robot
learning written by leading researchers in the field. As the
selection of papers illustrates, the field of robot learning is
both active and diverse. A variety of machine learning methods,
ranging from inductive logic programming to reinforcement learning,
is being applied to many subproblems in robot perception and
control, often with objectives as diverse as parameter calibration
and concept formulation. While no unified robot learning framework
has yet emerged to cover the variety of problems and approaches
described in these papers and other publications, a clear set of
shared issues underlies many robot learning problems. Machine
learning, when applied to robotics, is situated: it is embedded
into a real-world system that tightly integrates perception,
decision making and execution. Since robot learning involves
decision making, there is an inherent active learning issue.
Robotic domains are usually complex, yet the expense of using
actual robotic hardware often prohibits the collection of large
amounts of training data. Most robotic systems are real-time
systems. Decisions must be made within critical or practical time
constraints. These characteristics present challenges and
constraints to the learning system. Since these characteristics are
shared by other important real-world application domains, robotics
is a highly attractive area for research on machine learning. On
the other hand, machine learning is also highly attractive to
robotics. There is a great variety of open problems in robotics
that defy a static, hand-coded solution. Recent Advances in Robot
Learning is an edited volume of peer-reviewed original research
comprising seven invited contributions by leading researchers. This
research work has also been published as a special issue of Machine
Learning (Volume 23, Numbers 2 and 3).
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.
Recent Advances in Robot Learning contains seven papers on robot
learning written by leading researchers in the field. As the
selection of papers illustrates, the field of robot learning is
both active and diverse. A variety of machine learning methods,
ranging from inductive logic programming to reinforcement learning,
is being applied to many subproblems in robot perception and
control, often with objectives as diverse as parameter calibration
and concept formulation. While no unified robot learning framework
has yet emerged to cover the variety of problems and approaches
described in these papers and other publications, a clear set of
shared issues underlies many robot learning problems. Machine
learning, when applied to robotics, is situated: it is embedded
into a real-world system that tightly integrates perception,
decision making and execution. Since robot learning involves
decision making, there is an inherent active learning issue.
Robotic domains are usually complex, yet the expense of using
actual robotic hardware often prohibits the collection of large
amounts of training data. Most robotic systems are real-time
systems. Decisions must be made within critical or practical time
constraints. These characteristics present challenges and
constraints to the learning system. Since these characteristics are
shared by other important real-world application domains, robotics
is a highly attractive area for research on machine learning. On
the other hand, machine learning is also highly attractive to
robotics. There is a great variety of open problems in robotics
that defy a static, hand-coded solution. Recent Advances in Robot
Learning is an edited volume of peer-reviewed original research
comprising seven invited contributions by leading researchers. This
research work has also been published as a special issue of Machine
Learning (Volume 23, Numbers 2 and 3).
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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