Comparison of Inference Relations Defined over Different Sets of Ranking Functions
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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