Standpoint logic is a recently proposed formalism in the context of knowledge integration, which advocates a multi-perspective approach permitting reasoning with a selection of diverse and possibly conflicting standpoints rather than forcing their unification. In this paper, we introduce nested sequent calculi for propositional standpoint logics--proof systems that manipulate trees whose nodes are multisets of formulae--and show how to automate standpoint reasoning by means of non-deterministic proof-search algorithms. To obtain worst-case complexity-optimal proof-search, we introduce a novel technique in the context of nested sequents, referred to as "coloring," which consists of taking a formula as input, guessing a certain coloring of its subformulae, and then running proof-search in a nested sequent calculus on the colored input. Our technique lets us decide the validity of standpoint formulae in CoNP since proof-search only produces a partial proof relative to each permitted coloring of the input. We show how all partial proofs can be fused together to construct a complete proof when the input is valid, and how certain partial proofs can be transformed into a counter-model when the input is invalid. These "certificates" (i.e. proofs and counter-models) serve as explanations of the (in)validity of the input.
翻译:站点逻辑是最近在知识整合背景下提议的一种形式主义,它倡导一种多视角的方法,允许通过选择不同和可能相互冲突的观点进行推理,而不是强迫其统一。在本文中,我们引入了套式序列计算法,用于操纵那些其节点是多种公式组合的树木,并展示如何通过非确定性校对搜索算法来自动定位推理。为了获得最坏情况的复杂性和最佳的校对研究,我们引入了一种新型技术,即“彩色”,它包括以公式作为输入,猜测其子系统的某些颜色,然后在彩色输入的嵌入序列中进行校正搜索。我们的技术让我们通过非确定CONPS调查中的立场公式的有效性,因为证据研究只能产生相对于每种允许输入颜色的部分证据。我们展示了如何将所有部分证据结合在一起,以便在输入是有效的,这些输入的模型是无效的,这些部分证据是反的模型。这些模型是无效的。