一句话：提出用 Partial Max-SAT 从含噪执行轨迹中挖掘确定自动机，允许忽略最少故障观测且无需预处理。
从数据中挖掘模型在工业界应用广泛，但现有推断方法多假设行为完全确定，面对消息丢失或环境因素导致的噪声轨迹时效果不佳。本文提出一种容错模型挖掘方法，将含噪执行轨迹上的自动机推断编码为 Partial Max-SAT 问题，在保证自动机确定性的同时，允许忽略最少数量、最可能出错的观测，且无需对数据做预处理。作者评估了方法性能及若干实用考量，并在三个工业案例中推断出正确模型，表明该方法能有效处理实际噪声数据。
The mining of models from data finds widespread use in industry. There exists a variety of model inference methods for perfectly deterministic behaviour, however, in practice, the provided data often contains noise due to faults such as message loss or environmental factors that many of the inference algorithms have problems dealing with. We present a novel model mining approach using Partial Max-SAT solving to infer the best possible automaton from a set of noisy execution traces. This approach enables us to ignore the minimal number of presumably faulty observations to allow the construction of a deterministic automaton. No pre-processing of the data is required. The method's performance as well as a number of considerations for practical use are evaluated, including three industrial use cases, for which we inferred the correct models.