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 Pr\(\mathcal {A}\mathcal {L}\mathcal {C}\) as an example to compute the probability of the implicit knowledge.
Supported by the National Grand Fundamental Research 973 Program of China under Grant No.2002CB312006; the National Natural Science Foundation of China under Grant Nos. 60473058.
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Tao, J., Wen, Z., Hanpin, W., Lifu, W. (2007). PrDLs: A New Kind of Probabilistic Description Logics About Belief. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_64
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DOI: https://doi.org/10.1007/978-3-540-73325-6_64
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