在面向模型的数据细化中处理不确定性的另一种方法

Moshe Deutsch, Martin C. Henson
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引用次数: 0

摘要

本文研究了用正向和反向模拟的方法对由部分关系给出语义的面向模型规范进行数据细化。这种语义最著名的例子是z。精化的标准模型理论特征是基于总体化和提升的关系方法。我们对比了这一点,并将其与一种新的方法联系起来,这种方法受到代数范式和类型理论(改编为基于经典逻辑的框架)中的各种概念的启发,用于处理不确定性和数据细化中的规范。我们提供了一种基于提取一组(确定性的)“实现”,其中每个都对所讨论的规范建模。我们研究了数据模拟与这一概念相互作用的方法,并证明了每个随后的基于模拟的理论都相当于相应的标准关系特征的细化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An alternative approach to handling nondeterminism in model-oriented data-refinement
This paper investigates data-refinement by forward and backward simulations for model-oriented specifications whose semantics is given by partial relations. The most well-known example of such a semantics is that for Z. The standard model-theoretic characterisation of refinement is a relational approach based on totalisation and lifting. We contrast this, and relate it to, a new approach inspired by various concepts in algebraic paradigms and type theory (adapted into a framework based on classical logic) for handling nondeterminism and under specification in data-refinement. We provide a new characterisation of refinement based on extracting a set of (deterministic) "implementations", each of which models the specification in question. We examine a means by which data simulations interact with this notion and demonstrate that each of the ensuing simulation-based theories is equivalent to the corresponding standard relational characterisation of refinement.
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