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Characteristic Logics for Behavioural Metrics via Fuzzy Lax Extensions
arXiv - CS - Logic in Computer Science Pub Date : 2020-07-02 , DOI: arxiv-2007.01033
Paul Wild and Lutz Schr\"oder

Behavioural distances provide a fine-grained measure of equivalence in systems involving quantitative data, such as probabilistic, fuzzy, or metric systems. Like in the classical setting of crisp bisimulation-type equivalences, the wide variation found in system types creates a need for generic methods that apply to many system types at once. Approaches of this kind are emerging within the paradigm of universal coalgebra, based either on lifting pseudometrics along set functors or on lifting general real-valued (fuzzy) relations along functors by means of fuzzy lax extensions. An immediate benefit of the latter is that they allow bounding behavioural distance by means of fuzzy bisimulations that need not themselves be (pseudo-)metrics, in analogy to classical bisimulations (which need not be equivalence relations). We show that the known instances of generic pseudometric liftings, specifically the generic Kantorovich and Wasserstein liftings, both can be extended to yield fuzzy lax extensions, using the fact that both are effectively given by a choice of quantitative modalities. Our central result then shows that in fact all fuzzy lax extensions are Kantorovich extensions for a suitable set of quantitative modalities, the so-called Moss modalities. For non-expansive fuzzy lax extensions, this allows for the extraction of quantitative modal logics that characterize behavioural distance, i.e. satisfy a quantitative version of the Hennessy-Milner theorem; equivalently, we obtain expressiveness of a quantitative version of Moss' coalgebraic logic.

中文翻译:

通过模糊松散扩展的行为度量的特征逻辑

行为距离为涉及定量数据的系统(例如概率、模糊或公制系统)中的等价性提供了细粒度的度量。就像在清晰的互模拟类型等价的经典设置中一样,在系统类型中发现的广泛变化产生了对同时适用于许多系统类型的通用方法的需求。这种方法正在泛代数范式中出现,基于沿集合函子提升伪度量或通过模糊松散扩展沿函子提升一般实值(模糊)关系。后者的一个直接好处是它们允许通过模糊互模拟来限制行为距离,这些互模拟本身不需要是(伪)度量,类似于经典的互模拟(不需要是等价关系)。我们展示了通用伪度量提升的已知实例,特别是通用 Kantorovich 和 Wasserstein 提升,两者都可以扩展以产生模糊松散扩展,使用这两者都是通过选择定量模态有效给出的事实。然后,我们的中心结果表明,实际上所有模糊松散扩展都是针对一组合适的定量模态(即所谓的 Moss 模态)的 Kantorovich 扩展。对于非扩展模糊松散扩展,这允许提取表征行为距离的定量模态逻辑,即满足 Hennessy-Milner 定理的定量版本;等效地,我们获得了 Moss 代数逻辑的定量版本的表达能力。两者都可以扩展以产生模糊松散的扩展,因为两者都可以通过选择定量模式有效地给出。然后,我们的中心结果表明,实际上所有模糊松散扩展都是针对一组合适的定量模态(即所谓的 Moss 模态)的 Kantorovich 扩展。对于非扩展模糊松散扩展,这允许提取表征行为距离的定量模态逻辑,即满足 Hennessy-Milner 定理的定量版本;等效地,我们获得了 Moss 代数逻辑的定量版本的表达能力。两者都可以扩展以产生模糊松散的扩展,因为两者都可以通过选择定量模式有效地给出。然后,我们的中心结果表明,实际上所有模糊松散扩展都是针对一组合适的定量模态(即所谓的 Moss 模态)的 Kantorovich 扩展。对于非扩展模糊松散扩展,这允许提取表征行为距离的定量模态逻辑,即满足 Hennessy-Milner 定理的定量版本;等效地,我们获得了 Moss 代数逻辑的定量版本的表达能力。然后,我们的中心结果表明,实际上所有模糊松散扩展都是针对一组合适的定量模态(即所谓的 Moss 模态)的 Kantorovich 扩展。对于非扩展模糊松散扩展,这允许提取表征行为距离的定量模态逻辑,即满足 Hennessy-Milner 定理的定量版本;等效地,我们获得了 Moss 代数逻辑的定量版本的表达能力。然后,我们的中心结果表明,实际上所有模糊松散扩展都是针对一组合适的定量模态(即所谓的 Moss 模态)的 Kantorovich 扩展。对于非扩展模糊松散扩展,这允许提取表征行为距离的定量模态逻辑,即满足 Hennessy-Milner 定理的定量版本;等效地,我们获得了 Moss 代数逻辑的定量版本的表达能力。
更新日期:2020-07-03
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