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An Algorithm for Enumerating Maximal Models of Horn Theories with an Application to Modal Logics

机译:角理论最大模型的枚举算法及其在模态逻辑中的应用

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The fragment of propositional logic known as Horn theories plays a central role in automated reasoning. The problem of enumerating the maximal models of a Horn theory (MaxMod) has been proved to be computationally hard, unless P = NP. To the best of our knowledge, the only algorithm available for it is the one based on a brute-force approach. In this paper, we provide an algorithm for the problem of enumerating the maximal subsets of facts that do not entail a distinguished atomic proposition in a definite Horn theory (MaxNoEntail). We show that MaxMod is polynomially reducible to MaxNoEntail (and vice versa), making it possible to solve also the former problem using the proposed algorithm. Addressing MaxMod via MaxNoEntail opens, inter alia, the possibility of benefiting from the monotonicity of the notion of entail-ment. (The notion of model does not enjoy such a property.) We also discuss an application of MaxNoEntail to expressiveness issues for modal logics, which reveals the effectiveness of the proposed algorithm.
机译:命题逻辑的片段被称为“霍恩理论”,在自动推理中起着核心作用。除非P = NP,否则枚举Horn理论(MaxMod)的最大模型的问题已被证明在计算上比较困难。据我们所知,唯一可用的算法是基于蛮力方法的算法。在本文中,我们提供了一种算法,用于枚举在确定的Horn理论(MaxNoEntail)中不包含明显的原子命题的事实的最大子集。我们表明,MaxMod可多项式化为MaxNoEntail(反之亦然),这使得使用提出的算法也可以解决前一个问题。通过MaxNoEntail处理MaxMod尤其可以从继承概念的单调性中受益。 (模型的概念不具有这种性质。)我们还讨论了MaxNoEntail在模态逻辑的表达性问题中的应用,这揭示了所提出算法的有效性。

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