论文标题
学习模型检查和随机过程中信号时间逻辑的内核技巧
Learning Model Checking and the Kernel Trick for Signal Temporal Logic on Stochastic Processes
论文作者
论文摘要
我们在信号时间逻辑（STL）的公式上引入了相似性函数。它以内核函数的形式出现，在机器学习中众所周知是一种概念和计算高效的工具。相应的内核技巧使我们能够规避复杂的特征提取过程，即（通常是手动）识别公式的决定性属性的（通常是手动）的工作，以便可以应用学习。我们证明了这种后果及其在预测（定量）在随机过程上满意（定量）满意度的任务的优势：使用我们的内核和内核技巧，我们在计算上学习（i）有效地（ii）实际上确切的满意度预测指标，（iii）避免找到一种棘手的型号的态度，以实现形式的数字vectors noge a vectors a vectors noge a vectors noge a vectors a vectors a vectors noge a vectors noge n a vectors noge a vectors逐渐成分。我们支持通过理论上声音的PAC保证在实验中获得的高精度，从而确保我们的程序有效地提供了近距离的预测指标。
We introduce a similarity function on formulae of signal temporal logic (STL). It comes in the form of a kernel function, well known in machine learning as a conceptually and computationally efficient tool. The corresponding kernel trick allows us to circumvent the complicated process of feature extraction, i.e. the (typically manual) effort to identify the decisive properties of formulae so that learning can be applied. We demonstrate this consequence and its advantages on the task of predicting (quantitative) satisfaction of STL formulae on stochastic processes: Using our kernel and the kernel trick, we learn (i) computationally efficiently (ii) a practically precise predictor of satisfaction, (iii) avoiding the difficult task of finding a way to explicitly turn formulae into vectors of numbers in a sensible way. We back the high precision we have achieved in the experiments by a theoretically sound PAC guarantee, ensuring our procedure efficiently delivers a close-to-optimal predictor.
