Pigozzi Federico, Nenzi Laura, Medvet Eric
Department of Engineering and Architecture, University of Trieste, Trieste, Italy
Evol Comput. 2025 Mar 15;33(1):91-114. doi: 10.1162/evco_a_00347.
Describing the properties of complex systems that evolve over time is a crucial requirement for monitoring and understanding them. Signal Temporal Logic (STL) is a framework that proved to be effective for this aim because it is expressive and allows state properties as human-readable formulae. Crafting STL formulae that fit a particular system is, however, a difficult task. For this reason, a few approaches have been proposed recently for the automatic learning of STL formulae starting from observations of the system. In this paper, we propose BUSTLE (Bi-level Universal STL Evolver), an approach based on evolutionary computation for learning STL formulae from data. BUSTLE advances the state of the art because it (i) applies to a broader class of problems, in terms of what is known about the state of the system during its observation, and (ii) generates both the structure and the values of the parameters of the formulae employing a bi-level search mechanism (global for the structure, local for the parameters). We consider two cases where (a) observations of the system in both anomalous and regular state are available, or (b) only observations of regular state are available. We experimentally evaluate BUSTLE on problem instances corresponding to the two cases and compare it against previous approaches. We show that the evolved STL formulae are effective and human-readable: the versatility of BUSTLE does not come at the cost of lower effectiveness.
描述随时间演化的复杂系统的属性是监测和理解这些系统的关键要求。信号时序逻辑（STL）是一个已被证明对这一目标有效的框架，因为它具有表现力，并允许将状态属性表示为人类可读的公式。然而，构建适用于特定系统的STL公式是一项艰巨的任务。因此，最近已经提出了一些方法，用于从系统观测数据中自动学习STL公式。在本文中，我们提出了BUSTLE（双层通用STL演化器），这是一种基于进化计算从数据中学习STL公式的方法。BUSTLE推动了技术发展，因为它（i）在观测系统状态时，适用于更广泛的一类问题，（ii）采用双层搜索机制（全局搜索结构，局部搜索参数）生成公式的结构和参数值。我们考虑两种情况：（a）系统在异常状态和正常状态下的观测数据都可用，或者（b）仅正常状态的观测数据可用。我们通过实验评估了BUSTLE在对应于这两种情况的问题实例上的性能，并将其与以前的方法进行了比较。我们表明，演化得到的STL公式是有效且人类可读的：BUSTLE的通用性并没有以降低有效性为代价。
