To enhance understanding and collaboration with autonomous agents, it is crucial to construct a representation of their task strategies that integrates interpretability, monitoring, and formal reasoning. This dual-purpose representation fosters human comprehension and enables automated analytical processes. We achieve this balance by formalizing task strategies through temporal logic formulas. Recent trends emphasize inferring temporal logic formulas from data to explain system behaviors and assess autonomous agents’ competencies. Our methodology relies on positive and negative examples from system observations to construct a concise temporal logic formula consistent with the data. However, existing approaches often overlook real-world data’s noise and uncertainties, limiting practical deployment. Addressing this, we analyze labeled trajectories and aim to infer interpretable formulas that minimize misclassification loss. To tackle data uncertainties, we focus on labeled interval trajectories. Our algorithm maximizes the worst-case robustness margin, enhancing formula robustness and ensuring the adaptability and reliability of temporal logic inference in real-world applications.