I thought readers of the Uncertainty in AI List might be interested in this
book. For more information please visit http://mitpress.mit.edu/0262161680
Qualitative Methods for Reasoning under Uncertainty
Simon Parsons
In this book Simon Parsons describes qualitative methods for reasoning
under uncertainty, "uncertainty" being a catch-all term for various types
of imperfect information. The advantage of qualitative methods is that they
do not require precise numerical information. Instead, they work with
abstractions such as interval values and information about how values
change. The author does not invent completely new methods for reasoning
under uncertainty but provides the means to create qualitative versions of
existing methods. To illustrate this, he develops qualitative versions of
probability theory, possibility theory, and the Dempster-Shafer theory of
evidence.
According to Parsons, these theories are best considered complementary
rather than exclusive. Thus the book supports the contention that rather
than search for the one best method to handle all imperfect information,
one should use whichever method best fits the problem. This approach leads
naturally to the use of several different methods in the solution of a
single problem and to the complexity of integrating the results--a problem
to which qualitative methods provide a solution.
Simon Parsons is a Reader in the Department of Computer Science at the
University of Liverpool and the editor of the journal Knowledge Engineering
Review.
7 x 9, 514 pp.
cloth ISBN 0-262-16168-0
Jud Wolfskill
Associate Publicist
MIT Press
5 Cambridge Center, 4th Floor
Cambridge, MA 02142
617.253.2079
617.253.1709 fax
wolfskil@mit.edu
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