双相似性的密度游戏

IF 2 4区 计算机科学 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Yuichi Komorida, Shin-ya Katsumata, Nick Hu, Bartek Klin, Samuel Humeau, Clovis Eberhart, Ichiro Hasuo
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引用次数: 0

摘要

双相似性作为系统的等价概念一直是过程理论的核心。由于最近对定量系统(概率,加权,混合等)的兴趣增加,双相似性已经以各种方式扩展,例如概率系统之间的双模拟度量。双相似性的一个重要特征是它的博弈论特征,即剧透者和复制者相互对抗;将双相似游戏扩展到定量设置也得到了积极的追求。在本文中,我们提出了一个统一描述双相似概念的游戏特征的一般框架。我们的框架是用纤维和余代数形式化的。特别地,我们在纤维谓词转换方面的双相似性特征使我们能够推导出我们所谓的高密度双相似性博弈:双相似性的一般分类博弈特征。我们的框架涵盖了已知的类似双相似性的概念(如双仿真度量和双仿真半规范)以及新的概念(包括我们称之为双仿真拓扑)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Codensity Games for Bisimilarity

Codensity Games for Bisimilarity

Bisimilarity as an equivalence notion of systems has been central to process theory. Due to the recent rise of interest in quantitative systems (probabilistic, weighted, hybrid, etc.), bisimilarity has been extended in various ways, such as bisimulation metric between probabilistic systems. An important feature of bisimilarity is its game-theoretic characterization, where Spoiler and Duplicator play against each other; extension of bisimilarity games to quantitative settings has been actively pursued too. In this paper, we present a general framework that uniformly describes game characterizations of bisimilarity-like notions. Our framework is formalized categorically using fibrations and coalgebras. In particular, our characterization of bisimilarity in terms of fibrational predicate transformers allows us to derive what we call codensity bisimilarity games: a general categorical game characterization of bisimilarity. Our framework covers known bisimilarity-like notions (such as bisimulation metric and bisimulation seminorm) as well as new ones (including what we call bisimulation topology).

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来源期刊
New Generation Computing
New Generation Computing 工程技术-计算机:理论方法
CiteScore
5.90
自引率
15.40%
发文量
47
审稿时长
>12 weeks
期刊介绍: The journal is specially intended to support the development of new computational and cognitive paradigms stemming from the cross-fertilization of various research fields. These fields include, but are not limited to, programming (logic, constraint, functional, object-oriented), distributed/parallel computing, knowledge-based systems, agent-oriented systems, and cognitive aspects of human embodied knowledge. It also encourages theoretical and/or practical papers concerning all types of learning, knowledge discovery, evolutionary mechanisms, human cognition and learning, and emergent systems that can lead to key technologies enabling us to build more complex and intelligent systems. The editorial board hopes that New Generation Computing will work as a catalyst among active researchers with broad interests by ensuring a smooth publication process.
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