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Erschienen in:
2007 | OriginalPaper | Buchkapitel
PrDLs: A New Kind of Probabilistic Description Logics About Belief
verfasst von : Jia Tao, Zhao Wen, Wang Hanpin, Wang Lifu
Erschienen in: New Trends in Applied Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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It is generally accepted that knowledge based systems would be smarter if they can deal with uncertainty. Some research has been done to extend Description Logics(DLs) towards the management of uncertainty, most of which concerned the
statistical information
such as “The probability that a randomly chosen bird flies is greater than 0.9”. In this paper, we present a new kind of extended DLs to describe degrees of belief such as “The probability that all plastic objects float is 0.3”. We also introduce the extended tableau algorithm for Pr
$\mathcal {A}\mathcal {L}\mathcal {C}$
as an example to compute the probability of the implicit knowledge.