Abstract
Spohnian ranking functions are a qualitative abstraction of probability functions, and they have been applied to knowledge representation and reasoning that involve uncertainty. However, how to represent a ranking function which has a size that is exponential in the number of variables still remains insufficiently explored. In this work we introduce min-sum networks (MSNs) for a compact representation of ranking functions for multiple variables. This representation allows for exact inference with linear cost in the size of the number of nodes .
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Acknowledgement
KK, MT, TR and XS acknowledge funding of the German Science Foundation (DFG) via the project “CAML”, KE 1686/3-1. ZY and KK acknowledge the funding of the German Federal Ministry of Education and Research (BMBF) project “MADESI”, 01IS18043B. AS and KK acknowledge the support of the BMBF and the Hessian Ministry of Science and the Arts (HMWK) within the National Research Center for Applied Cybersecurity ATHENE.
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Shao, X., Yu, Z., Skryagin, A., Rienstra, T., Thimm, M., Kersting, K. (2020). Modelling Multivariate Ranking Functions with Min-Sum Networks. In: Davis, J., Tabia, K. (eds) Scalable Uncertainty Management. SUM 2020. Lecture Notes in Computer Science(), vol 12322. Springer, Cham. https://doi.org/10.1007/978-3-030-58449-8_22
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DOI: https://doi.org/10.1007/978-3-030-58449-8_22
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