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Foundations of Knowledge Acquisition - Cognitive Models of Complex Learning (Paperback, Softcover reprint of the original 1st ed. 1993)
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Foundations of Knowledge Acquisition - Cognitive Models of Complex Learning (Paperback, Softcover reprint of the original 1st ed. 1993)
Series: The Springer International Series in Engineering and Computer Science, 194
Expected to ship within 10 - 15 working days
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The two volumes of Foundations of Knowledge Acquisition document
the recent progress of basic research in knowledge acquisition
sponsored by the Office of Naval Research. This volume is subtitled
Cognitive Models of Complex Learning, and there is a companion
volume, subtitles Machine Learning. Funding was provided by a
five-year Accelerated Research Initiative (ARI), and made possible
significant advances in the scientific understanding of how
machines and humans can acquire new knowledge so as to exhibit
improved problem-solving behavior. Knowledge acquisition, as
persued under the ARI, was a coordinated research thrust into both
machine learning and the human learning. Chapters in Cognitive
Models of Complex Learning thus include summaries of work by
cognitive scientists who do computational modeling of human
learning. In fact, an accomplishment of research previously
sponsored by ONR's Cognitive Science Program gave insight into the
knowledge and skills that distinguish human novices from human
experts in various domains; the cognitive interest in the ARI was
then to characterize how the transition form novice to expert
actually takes place.Chapters particularly relevant to that concern
are those written by Anderson, Kieras, Marshall, Ohlsson, and
VanLehn. Significant progress in machine learning is reported along
in a variety of fronts in the companion volume, Machine Learning,
also published by Kluwer Academic Publishers. Included is work in
analogical reasoning; induction and discovery; explanation-based
learning; learning by competition, using genetic algorithms;
learning within natural language systems; theoretical limitations,
learning in Soar, a proposed general architecture for intelligent
systems; and case-based reasoning. These volumes of Foundations of
Knowledge Acquisition are excellent reference sources by bringing
together descriptions of recent and ongoing research at the
forefront of progress in one the most challenging arenas of
artificial intelligence and cognitive science. In addition,
contributing authors comment on ecxiting future directions for
research.
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