What is it about?

We cannot trust complex systems, such as robots or autonomous vehicles, to behave exactly as we would have expected during their design phase. Hence, learning properties satisfied by their executions is paramount to either finding bugs in their implementation or gaining trust in their way of operating.

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Why is it important?

This work is the first one that automatically learns generic temporal relations between different executions of a system. The method is highly flexible as it allows the user to embed as much prior knowledge as desired in the learning process.

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This page is a summary of: Mining Hyperproperties using Temporal Logics, ACM Transactions on Embedded Computing Systems, September 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3609394.
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