Abstract

Probabilistic model checking (PMC) is a well-established method for the quantitative analysis of state based operational models such as Markov decision processes. Description logics (DLs) provide a well-suited formalism to describe and reason about knowledge and are used as basis for the web ontology language (OWL). We investigate how such knowledge described by DLs can be integrated into the PMC process, introducing ontology-mediated PMC. Specifically, we propose ontologized programs as a formalism that links ontologies to behaviors specified by probabilistic guarded commands, the de-facto standard input formalism for PMC tools such as Prism. Through DL reasoning, inconsistent states in the modeled system can be detected. We present three ways to resolve these inconsistencies, leading to different Markov decision process semantics. We analyze the computational complexity of checking whether an ontologized program is consistent under these semantics. Further, we present and implement a technique for the quantitative analysis of ontologized programs relying on standard DL reasoning and PMC tools. This way, we enable the application of PMC techniques to analyze knowledge-intensive systems.We evaluate our approach and implementation on amulti-server systemcase study,where different DL ontologies are used to provide specifications of different server platforms and situations the system is executed in.

中文翻译：

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使用本体增强概率模型检查

摘要

概率模型检查 (PMC) 是一种行之有效的方法，用于定量分析基于状态的操作模型，例如马尔可夫决策过程。描述逻辑 (DL) 提供了一种非常适合的形式来描述和推理知识，并被用作 Web 本体语言 (OWL) 的基础。我们研究了如何将 DL 描述的这些知识集成到 PMC 过程中，引入本体介导的PMC。具体来说，我们提出本体化程序作为将本体与概率保护命令指定的行为联系起来的一种形式，PMC 工具（如 Prism）的事实上的标准输入形式。通过深度学习推理，可以检测到建模系统中的不一致状态。我们提出了三种解决这些不一致的方法，从而导致不同的马尔可夫决策过程语义。我们分析了在这些语义下检查本体化程序是否一致的计算复杂性。此外，我们提出并实施了一种技术，用于依赖标准 DL 推理和 PMC 工具对本体化程序进行定量分析。通过这种方式，我们能够应用 PMC 技术来分析知识密集型系统。我们在多服务器系统案例研究中评估我们的方法和实施，