Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach
Jean-Raphaël Gaglione,
Daniel Neider,
Rajarshi Roy
et al.
Abstract:We address the problem of inferring descriptions of system behavior using Linear Temporal Logic (LTL) from a finite set of positive and negative examples. Most of the existing approaches for solving such a task rely on predefined templates for guiding the structure of the inferred formula. The approaches that can infer arbitrary LTL formulas, on the other hand, are not robust to noise in the data. To alleviate such limitations, we devise two algorithms for inferring concise LTL formulas even in the presence of… Show more
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