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Probabilistic reasoning in the description logic \(\mathcal {ALCP}\) with the principle of maximum entropy. (English) Zbl 1366.68293
Schockaert, Steven (ed.) et al., Scalable uncertainty management. 10th international conference, SUM 2016, Nice, France, September 21–23, 2016. Proceedings. Cham: Springer (ISBN 978-3-319-45855-7/pbk; 978-3-319-45856-4/ebook). Lecture Notes in Computer Science 9858. Lecture Notes in Artificial Intelligence, 246-259 (2016).
Summary: A central question for knowledge representation is how to encode and handle uncertain knowledge adequately. We introduce the probabilistic description logic \(\mathcal {ALCP}\) that is designed for representing context-dependent knowledge, where the actual context taking place is uncertain. \(\mathcal {ALCP}\) allows the expression of logical dependencies on the domain and probabilistic dependencies on the possible contexts. In order to draw probabilistic conclusions, we employ the principle of maximum entropy. We provide reasoning algorithms for this logic, and show that it satisfies several desirable properties of probabilistic logics.
For the entire collection see [Zbl 1344.68017].

68T27 Logic in artificial intelligence
68T30 Knowledge representation
68T37 Reasoning under uncertainty in the context of artificial intelligence
Full Text: DOI
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