Abstract
Background knowledge is often represented by sets of conditionals of the form “if A then usually B”. Such knowledge bases should not be circuitous, but compact and easy to compare in order to allow for efficient processing in approaches dealing with and inferring from background knowledge, such as nonmonotonic reasoning. In this paper we present transformation systems on conditional knowledge bases that allow to identify and remove unnecessary conditionals from the knowledge base while preserving the knowledge base’s model set.
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Acknowledgment
This work was supported by DFG-Grant KI1413/5-1 to Gabriele Kern-Isberner as part of the priority program “New Frameworks of Rationality” (SPP 1516). Christian Eichhorn is supported by this Grant.
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Beierle, C., Eichhorn, C., Kern-Isberner, G. (2017). On Transformations and Normal Forms of Conditional Knowledge Bases. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_53
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DOI: https://doi.org/10.1007/978-3-319-60042-0_53
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