Abstract
A conditional knowledge base \(\mathcal {R}\) contains defeasible rules of the form “If A, then usually B”. For the notion of c-representations, a skeptical inference relation taking all c-representations of \(\mathcal {R}\) into account has been suggested. In this paper, we propose a 3-phase compilation scheme for both knowledge bases and skeptical queries to constraint satisfaction problems. In addition to skeptical c-inference, we show that also credulous and weakly skeptical c-inference can be modelled as a constraint satisfaction problem, and that the compilation scheme can be extended to such queries. For each compilation step, we prove its soundness and completeness, and demonstrate significant efficiency benefits when querying the compiled version of \(\mathcal {R}\). These findings are also supported by experiments with the software system InfOCF that employs the proposed compilation scheme.
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Acknowledgements
This work was supported by DFG Grant BE 1700/9-1 of Prof. Dr. Christoph Beierle as part of the priority program “Intentional Forgetting in Organizations” (SPP 1921). Kai Sauerwald is supported by this Grant. We thank the anonymous reviewers for their valuable hints and comments that helped us to improve the paper.
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Beierle, C., Kutsch, S., Sauerwald, K. (2018). Compilation of Conditional Knowledge Bases for Computing C-Inference Relations. In: Ferrarotti, F., Woltran, S. (eds) Foundations of Information and Knowledge Systems. FoIKS 2018. Lecture Notes in Computer Science(), vol 10833. Springer, Cham. https://doi.org/10.1007/978-3-319-90050-6_3
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