论文标题
学习时间逻辑属性：两种最新方法的概述
Learning Temporal Logic Properties: an Overview of Two Recent Methods
论文作者
论文摘要
从标记为正或负面的示例中学习线性时间逻辑（LTL）公式在推断系统行为描述时发现了应用。我们总结了从两个不同问题设置中的示例中学习LTL公式的两种方法。第一种方法在示例的标签中假设噪声。为此，他们定义了推断LTL公式必须与大多数但不是全部示例一致的问题。第二种方法考虑了在仅给出积极示例的情况下推断有意义的LTL公式的另一个问题。因此，第一种方法解决了噪声的鲁棒性，第二种方法解决了被推论公式的简洁性和特异性（即语言最小值）之间的平衡。汇总方法提出了不同的算法来解决上述问题，并推断了时间属性的其他描述，例如信号时间逻辑或确定性有限自动机。
Learning linear temporal logic (LTL) formulas from examples labeled as positive or negative has found applications in inferring descriptions of system behavior. We summarize two methods to learn LTL formulas from examples in two different problem settings. The first method assumes noise in the labeling of the examples. For that, they define the problem of inferring an LTL formula that must be consistent with most but not all of the examples. The second method considers the other problem of inferring meaningful LTL formulas in the case where only positive examples are given. Hence, the first method addresses the robustness to noise, and the second method addresses the balance between conciseness and specificity (i.e., language minimality) of the inferred formula. The summarized methods propose different algorithms to solve the aforementioned problems, as well as to infer other descriptions of temporal properties, such as signal temporal logic or deterministic finite automata.
