Learning Linear Temporal Specifications from Demonstrations with Uncertainty

Learning temporal logic specifications from system demonstrations is essential for tasks such as formal verification and controller synthesis, especially in safety-critical domains. Existing approaches typically assume demonstrations are correct or only affected by misclassification errors. In practice, however, system traces are often uncertain or incomplete due to sensor faults, measurement errors, or data loss. We present a framework for learning minimal Linear Temporal Logic (LTL) formulas f

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ethics.ai (12 July 2026), “Learning Linear Temporal Specifications from Demonstrations with Uncertainty,” evidence record 27, https://ethics.ai/record/27 (originally published by arXiv).

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