PrDLs: a new kind of probabilistic description logics about belief

  • Authors:

  • Jia Tao;Zhao Wen;Wang Hanpin;Wang Lifu

  • Affiliations:

  • School of Electronics Engineering and Computer Science, Peking Univ., Beijing, China;School of Electronics Engineering and Computer Science, Peking Univ., Beijing, China;School of Electronics Engineering and Computer Science, Peking Univ., Beijing, China;School of Electronics Engineering and Computer Science, Peking Univ., Beijing, China

  • Venue:
  • IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
  • Year:
  • 2007

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Abstract

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 PrALC as an example to compute the probability of the implicit knowledge.