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arXiv 1909.08017cs.LO
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基于增量归纳的Markov链的可达性

Verifying Reachability Properties in Markov Chains via Incremental Induction

Elizabeth Polgreen, Martin Brain, Martin Fraenzle, Alessandro Abate

摘要:

在概率验证和非概率验证之间存在可扩展性差距。概率模型检验工具基于显式引擎或(多端)二元决策图。这些结构在非概率验证领域得到了更可扩展的技术(如IC3)的补充。我们提出了一个符号概率模型检验算法的基础上IC3类增量建设的归纳条款划分的状态空间,交错与增量建设的线性不等式系统。本文比较了我们的实现标准的定量验证的替代方案:我们的实验表明,我们的算法是一个步骤,以更可扩展的符号验证有限状态马尔可夫链中的罕见事件。

英文摘要:

There is a scalability gap between probabilistic and non-probabilistic verification. Probabilistic model checking tools are based either on explicit engines or on (Multi-Terminal) Binary Decision Diagrams. These structures are complemented in areas of non-probabilistic verification by more scalable techniques, such as IC3. We present a symbolic probabilistic model checking algorithm based on IC3-like incremental construction of inductive clauses to partition the state space, interleaved with incremental construction of a system of linear inequalities. This paper compares our implementation to standard quantitative verification alternatives: our experiments show that our algorithm is a step to more scalable symbolic verification of rare events in finite-state Markov chains.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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