Abstract
In most knowledge-based systems, the guarantee of consistency is one of the essential tasks to ensure them to avoid the trivial cases. Because of this reason, a wide range of approaches has been proposed for restoring consistency. However, these approaches often correspond to logical, or probabilistic-logical framework. In this paper, we investigate a model for restoring the consistency of probabilistic knowledge bases by focusing on the method of changing the probabilities in such knowledge bases. To this aim, a process to restore the consistency based on inconsistency measures is introduced, a set of rational and intuitive axioms to characterize the restoring operators is proposed, and several logical properties are investigated and discussed.
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Acknowledgment
The authors would like to thank Professor Quang Thuy Ha, Faculty of Information Technology, Hanoi University of Engineering and Technology, Vietnam and Professor Ngoc Thanh Nguyen, Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Poland for their expertise support.
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Nguyen, V.T., Tran, T.H. (2018). Solving Inconsistencies in Probabilistic Knowledge Bases via Inconsistency Measures. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10751. Springer, Cham. https://doi.org/10.1007/978-3-319-75417-8_1
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