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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.
In early 1986, one of us (D.M.S.) was constructing an artificial
intelligence system to design algorithms, and the other (A.P.A.)
was getting started in program transformations research. We shared
an office, and exchanged a few papers on the systematic development
of algorithms from specifications. Gradually we realized that we
were trying to solve some of the same problems. And so, despite
radical differences between ourselves in research approaches, we
set out together to see what we could learn from these papers.
That's how this book started: a couple of graduate students trying
to cope with The Literature. At first, there was just a list of
papers. One of us (D.M.S.) tried to cast the papers in a uniform
framework by describing the problem spaces searched, an approach
used in artificial intelligence for understanding many tasks. The
generalized problem space descriptions, though useful, seemed to
abstract too much, so we decided to compare papers by different
authors dealing with the same algorithm. These comparisons proved
crucial: for then we began to see similar key design choices for
each algorithm.
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