Abstract
Splitting belief bases is fundamental for efficient reasoning and for better understanding interrelationships among the knowledge entities. In this paper, we survey the most important splitting techniques for conditional belief bases in the context of c-representations which constitute a specific class of ranking models with outstanding behavior not only with respect to belief base splitting, as shown in recent papers. We provide a splitting hierarchy, in particular by proving that safe conditional syntax splittings and case splittings are so-called CSP-constraint splittings. We advance the level of knowledge about CSP-constraint splittings and present an algorithm for computing CSP-constraint splittings.
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Notes
- 1.
The entries of the concatenated solution vectors have to be reordered such that they fit the ordering of the conditionals in \(\varDelta \).
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Acknowledgments
This work was supported by grants of the German Research Foundation (DFG) awarded to Gabriele Kern-Isberner (KE 1413/14-1) and to Christoph Beierle (BE 1700/10-1).
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Wilhelm, M., Sezgin, M., Kern-Isberner, G., Haldimann, J., Beierle, C., Heyninck, J. (2023). Splitting Techniques for Conditional Belief Bases in the Context of c-Representations. In: Gaggl, S., Martinez, M.V., Ortiz, M. (eds) Logics in Artificial Intelligence. JELIA 2023. Lecture Notes in Computer Science(), vol 14281. Springer, Cham. https://doi.org/10.1007/978-3-031-43619-2_32
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