Numerically Representing Stochastic Process Algebra Models

Numerically Representing Stochastic Process Algebra Models
复制标题

随机过程代数模型的数值表示

DOI:
10.1093/comjnl/bxs013
复制
发表时间:
2012-11
期刊:
影响因子:
1.4
通讯作者:
Jane Hillston
Jane Hillston
中科院分区:
计算机科学4区
文献类型:
--
作者:
Jie Ding;Jane Hillston

文献摘要

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随机过程代数结合了基于相互作用成分的高级系统描述和基于随机过程的严格的低级数学模型。这些已被证明是有价值的建模形式,特别是在性能建模和系统生物学领域。然而,它们确实存在状态空间爆炸的问题。目前,潜在的随机过程通常是通过过程代数的小步运算语义推导出来的,并依赖于过程状态的语法表示。本文提出了一种基于计数抽象的数字表示模式。这将基于复制的组件自动检测状态空间中的对称性,并生成一个紧凑的状态空间。此外,正如我们所证明的那样,它适用于其他解释,因此也适用于其他形式的计算分析,从而丰富了可以从模型中导出的定性和定量测量集。
Stochastic process algebras combine a high-level system description in terms of interacting components, with a rigorous low-level mathematical model in terms of a stochastic process. These have proved to be valuable modelling formalisms, particularly in the areas of performance modelling and systems biology. However, they do suffer from the problem of state space explosion. Currently, the underlying stochastic process is generally derived via the small step operational semantics of the process algebra and relies on a syntactical representation of the states of the process. In this paper, we propose a numerical representation schema based on a counting abstraction. This automatically detects symmetries within the state space based on replicated components, and produces a compact state space. Moreover, as we demonstrate, it is amenable to other interpretations and thus other forms of computational analysis, enriching the set of qualitative and quantitative measures that can be derived from a model.
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