基于数据驱动方法，用概率保证评估系统在有限步内的安全性。
Data-Driven Probabilistic Evaluation of Logic Properties with PAC-Confidence on Mealy Machines
- 通过主动学习收集数据，结合PAC学习框架估算安全概率。
- 在有限时间步内，以高置信度给出系统安全性的概率评估。
- 适用于无法获取精确模型的复杂系统，如自动驾驶车辆。
网络物理系统（CPS）是复杂的系统，需要强大的模型来完成验证、诊断或调试等任务。通常难以获得合适的模型，且手动建模困难。数据驱动方法可基于系统采集的数据解决诊断和验证问题。本文研究具有离散抽象形式的Mealy机器的CPS，提出一种数据驱动方法，在n个时间步的有限时域内确定系统的安全概率。该方法基于“可能近似正确”（PAC）学习范式，建立了离散逻辑与系统概率可达性分析之间的联系，并为计算出的概率提供额外置信度。学习过程采用主动学习策略：在初始数据集采集后，以引导方式生成新的学习数据。通过自动驾驶车道保持系统的案例研究验证了该方法的有效性。
原文摘要 · Abstract (English)
Cyber-Physical Systems (CPS) are complex systems that require powerful models for tasks like verification, diagnosis, or debugging. Often, suitable models are not available and manual extraction is difficult. Data-driven approaches then provide a solution to, e.g., diagnosis tasks and verification problems based on data collected from the system. In this paper, we consider CPS with a discrete abstraction in the form of a Mealy machine. We propose a data-driven approach to determine the safety probability of the system on a finite horizon of n time steps. The approach is based on the Probably Approximately Correct (PAC) learning paradigm. Thus, we elaborate a connection between discrete logic and probabilistic reachability analysis of systems, especially providing an additional confidence on the determined probability. The learning process follows an active learning paradigm, where new learning data is sampled in a guided way after an initial learning set is collected. We validate the approach with a case study on an automated lane-keeping system.
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