The framework of quantitative equational logic has been successfully applied to reason about algebras whose carriers are metric spaces and operations are nonexpansive. We extend this framework in two orthogonal directions: algebras endowed with generalised metric space structures, and operations being nonexpansive up to a lifting. We apply our results to the algebraic axiomatisation of the {\L}ukaszyk--Karmowski distance on probability distributions, which has recently found application in the field of representation learning on Markov processes.
翻译:量化等式逻辑框架已成功地应用于代数推理,其代数的载体是可计量空间,操作是非探索性的。我们将这一框架扩展为两个正方位方向:具有通用计量空间结构的代数,操作是非探索性的,直到升空。我们把结果应用到 prukaszyk-Karmowski 的概率分布的代数轴化中,该值最近应用于Markov 过程的代表学习领域。