Model checking systems formalized using probabilistic models such as discrete time Markov chains (DTMCs) and Markov decision processes (MDPs) can be reduced to computing constrained reachability properties. Linear programming methods to compute reachability probabilities for DTMCs and MDPs do not scale to large models. Thus, model checking tools often employ iterative methods to approximate reachability probabilities. These approximations can be far from the actual probabilities, leading to inaccurate model checking results. On the other hand, specialized techniques employed in existing state-of-the-art exact quantitative model checkers, don’t scale as well as their iterative counterparts. In this work, we present a new model checking algorithm that improves the approximate results obtained by scalable iterative techniques to compute exact reachability probabilities. Our techniques are implemented as an extension of the PRISM model checker and are evaluated against other exact quantitative model checking engines.

中文翻译：

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通过理性搜索进行精确的定量概率模型检查

使用诸如离散时间马尔可夫链 (DTMC) 和马尔可夫决策过程 (MDP) 等概率模型形式化的模型检查系统可以简化为计算受限可达性属性。用于计算 DTMC 和 MDP 的可达性概率的线性规划方法不能扩展到大型模型。因此，模型检查工具通常采用迭代方法来近似可达概率。这些近似值可能与实际概率相差甚远，从而导致模型检查结果不准确。另一方面，现有最先进的精确定量模型检查器中采用的专业技术的扩展性不如其迭代对应物。在这项工作中，我们提出了一种新的模型检查算法，该算法改进了通过可扩展迭代技术获得的近似结果，以计算精确的可达性概率。我们的技术是作为 PRISM 模型检查器的扩展实现的，并针对其他精确的定量模型检查引擎进行评估。