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- W2807161818 abstract "We present two novel algorithms for learning formulas in Linear Temporal Logic (LTL) from examples. The first learning algorithm reduces the learning task to a series of satisfiability problems in propositional Boolean logic and produces a smallest LTL formula (in terms of the number of subformulas) that is consistent with the given data. Our second learning algorithm, on the other hand, combines the SAT-based learning algorithm with classical algorithms for learning decision trees. The result is a learning algorithm that scales to real-world scenarios with hundreds of examples, but can no longer guarantee to produce minimal consistent LTL formulas. We compare both learning algorithms and demonstrate their performance on a wide range of synthetic benchmarks. Additionally, we illustrate their usefulness on the task of understanding executions of a leader election protocol." @default.
- W2807161818 created "2018-06-13" @default.
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- W2807161818 date "2018-10-01" @default.
- W2807161818 modified "2023-09-29" @default.
- W2807161818 title "Learning Linear Temporal Properties" @default.
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- W2807161818 doi "https://doi.org/10.23919/fmcad.2018.8603016" @default.
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