Monitors That Learn From Failures: Pairing STL and Genetic Programming

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@Article{Brunello:2023:ACC,

• author = "Andrea Brunello and Dario Della Monica and Angelo Montanari and Nicola Saccomanno and Andrea Urgolo",
• journal = "IEEE Access",
• title = "Monitors That Learn From Failures: Pairing {STL} and Genetic Programming",
• year = "2023",
• volume = "11",
• pages = "57349--57364",
• abstract = "In several domains, systems generate continuous streams of data during their execution, including meaningful telemetry information, that can be used to perform tasks like preemptive failure detection. Deep learning models have been exploited for these tasks with increasing success, but they hardly provide guarantees over their execution, a problem which is exacerbated by their lack of interpretability. In many critical contexts, formal methods, which ensure the correct behaviour of a system, are thus necessary. However, specifying in advance all the relevant properties and building a complete model of the system against which to check them is often out of reach in real-world scenarios. To overcome these limitations, we design a framework that resorts to monitoring, a lightweight runtime verification technique that does not require an explicit model specification, and pairs it with machine learning. Its goal is to automatically derive relevant properties, related to a bad behaviour of the considered system, encoded by means of formulas of Signal Temporal Logic ( $\mathsf {STL}$ ). Results based on experiments performed on well-known benchmark datasets show that the proposed framework is able to effectively anticipate critical system behaviours in an online setting, providing human-interpretable results.",
• keywords = "genetic algorithms, genetic programming, Monitoring, Machine learning, Task analysis, Feature extraction, Runtime, Data mining, Telemetry, Failure analysis, Machine learning, formal methods, runtime verification, monitoring, failure detection, explainable AI, XAI",
• DOI = "doi:10.1109/ACCESS.2023.3277620",
• ISSN = "2169-3536",
• notes = "Also known as \cite{10129205}",

}

Genetic Programming entries for Andrea Brunello Dario Della Monica Angelo Montanari Nicola Saccomanno Andrea Urgolo

Citations