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Mathematical Models for Handling Partial Knowledge in Artificial Intelligence (Paperback, Softcover reprint of the original 1st ed. 1995)
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Mathematical Models for Handling Partial Knowledge in Artificial Intelligence (Paperback, Softcover reprint of the original 1st ed. 1995)
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Knowledge acquisition is one of the most important aspects
influencing the quality of methods used in artificial intelligence
and the reliability of expert systems. The various issues dealt
with in this volume concern many different approaches to the
handling of partial knowledge and to the ensuing methods for
reasoning and decision making under uncertainty, as applied to
problems in artificial intelligence. The volume is composed of the
invited and contributed papers presented at the Workshop on
Mathematical Models for Handling Partial Knowledge in Artificial
Intelligence, held at the Ettore Majorana Center for Scientific
Culture of Erice (Sicily, Italy) on June 19-25, 1994, in the
framework of the International School of Mathematics
"G.Stampacchia." It includes also a transcription of the roundtable
held during the workshop to promote discussions on fundamental
issues, since in the choice of invited speakers we have tried to
maintain a balance between the various schools of knowl edge and
uncertainty modeling. Choquet expected utility models are discussed
in the paper by Alain Chateauneuf: they allow the separation of
perception of uncertainty or risk from the valuation of outcomes,
and can be of help in decision mak ing. Petr Hajek shows that
reasoning in fuzzy logic may be put on a strict logical (formal)
basis, so contributing to our understanding of what fuzzy logic is
and what one is doing when applying fuzzy reasoning."
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