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中文摘要：
随机的模型检查是检查的古典模型的最近的延期和归纳，它集中于检查一个系统模型的时间的性质的份量上。PCTL * 是重要量的性质说明语言之一，它与概率界限严格地是比 PCTL (概率的计算树逻辑) 或 LTL (线性时间的逻辑) 更富有表达力的。目前， PCTL * 随机的模型检查算法是很复杂的，并且不能提供一个公式为什么或不控制一个给定的模型的任何相关解释。为处理这个问题，为 PCTL 的一条直觉、简明的途径 * 随机的模型与证据检查在这份报纸被提出，它包括：为 PCTL 介绍比赛语义 * 在 release-PNF (版本积极的正常形式) ，定义 PCTL * 随机的模型检查游戏，用策略在比赛解决完成 PCTL * 随机的模型检查，并且作为证据精制赢的策略证明随机的模型检查结果。稳固和基于比赛的 PCTL 的完全性 * 随机的模型检查被证明，并且它的复杂性匹配知道更低、上面的界限。基于比赛的 PCTL * 随机的模型检查算法在一个视觉原型工具中被实现，并且它的可行性被一个解说性的例子表明。
英文摘要：
Stochastic model checking is a recent extension and generalization of the classical model checking, which focuses on quantitatively checking the temporal property of a system model. PCTL＊ is one of the important quantitative property specification languages, which is strictly more expressive than either PCTL （probabilistic computation tree logic） or LTL （linear temporal logic） with probability bounds. At present, PCTL＊ stochastic model checking algorithm is very complicated, and cannot provide any relevant explanation of why a formula does or does not hold in a given model. For dealing with this problem, an intuitive and succinct approach for PCTL＊ stochastic model checking with evidence is put forward in this paper, which includes： presenting the game semantics for PCTL＊ in release-PNF （release-positive normal form）, defining the PCTL＊ stochastic model checking game, using strategy solving in game to achieve the PCTL＊ stochastic model checking, and refining winning strategy as the evidence to certify stochastic model checking result. The soundness and the completeness of game-based PCTL＊ stochastic model checking are proved, and its complexity matches the known lower and upper bounds. The game-based PCTL＊ stochastic model checking algorithm is implemented in a visual prototype tool, and its feasibility is demonstrated by an illustrative example.
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