Comparison of Inference Relations Defined over Different Sets of Ranking Functions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)


Skeptical inference in the context of a conditional knowledge base \(\mathcal R\) can be defined with respect to a set of models of \(\mathcal R\). For the semantics of ranking functions that assign a degree of surprise to each possible world, we develop a method for comparing the inference relations induced by different sets of ranking functions. Using this method, we address the problem of ensuring the correctness of approximating c-inference for \(\mathcal R\) by constraint satisfaction problems (CSPs) over finite domains. While in general, determining a sufficient upper bound for these CSPs is an open problem, for a sequence of simple knowledge bases investigated only experimentally before, we prove that using the number of conditionals in \(\mathcal R\) as an upper bound correctly captures skeptical c-inference.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of HagenHagenGermany

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