Falsification of Conditional Safety Properties for Cyber-Physical Systems with Gaussian Process Regression
Falsification of Conditional Safety Properties for Cyber-Physical Systems with Gaussian Process Regression
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DOI：
10.1007/978-3-319-46982-9_27
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发表时间：
2016-09
期刊：
影响因子：
4.5
通讯作者：
Takumi Akazaki
中科院分区：
文献类型：
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作者：
Takumi Akazaki
我们提出了一个框架来解决条件安全属性的伪造问题，即“只要先行条件成立，安全属性就成立”的规范。在概要中，我们的框架遵循基于鲁棒语义和数值优化的现有框架。也就是说，我们通过迭代以下过程来搜索反例输入：（1）选取一个输入； (2) 测试在当前输入下满足规范的稳健程度； (3)再次选择一个新的输入，希望具有较小的鲁棒性。在条件安全属性的伪造中，现有算法的问题之一如下：我们有时迭代地选取不满足先行条件的输入，并且相应的测试变得信息量较少。为了克服这个问题，我们采用高斯过程回归（模型估计技术之一）并估计先行条件以高概率成立的输入搜索空间的区域。
We propose a framework to solve falsification problems ofconditional safety properties—specifications such that “a safety propertyholds whenever an antecedent conditionholds.” In the outline, our framework follows the existing one based onrobust semanticsand numerical optimization. That is, we search for a counterexample input by iterating the following procedure: (1) pick up an input; (2) test how robustly the specification is satisfied under the current input; and (3) pick up a new input again hopefully with a smaller robustness. In falsification of conditional safety properties, one of the problems of the existing algorithm is the following: we sometimes iteratively pick up inputs that do not satisfy the antecedent condition, and the corresponding tests become less informative. To overcome this problem, we employGaussian process regression—one of the model estimation techniques—and estimate the region of the input search space in which the antecedent conditionholds with high probability.