发表机构
The Pennsylvania State University; School of Electrical Engineering and Computer Science at The Pennsylvania State University（宾夕法尼亚州立大学; 宾夕法尼亚州立大学电气工程与计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究从含不确定性的演示中学习线性时态规范，核心方法是通过汉明距离建模不确定性，围绕观察轨迹生成估计值并分组约束，主要贡献是所提方法能在不确定性下恢复更接近真实公式的规范，优于现有LTL学习方法。
AI 中文摘要
从系统演示中学习时态逻辑规范对于形式验证和控制器综合等任务至关重要，尤其是在安全关键领域。现有方法通常假定演示是正确的或仅受错误分类误差影响。然而在实际中，由于传感器故障、测量误差或数据丢失，系统轨迹往往不确定或不完整。我们提出了一个从具有不确定性的演示中学习最小线性时态逻辑（LTL）公式的框架。我们的方法通过汉明距离对不确定性建模，围绕每个观察到的轨迹生成可能的估计值，并通过约束将其分组，要求每组中至少有一个轨迹与学习到的公式一致。然后我们的问题被简化为一个等效的伪布尔优化问题。我们将我们的方法与最先进的LTL学习方法进行评估比较，结果表明在不确定性情况下，我们的方法恢复的规范与真实公式更接近。
英文摘要
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 from demonstrations with uncertainty. Our approach models uncertainty via Hamming distance to generate possible estimates around each observed trace, which are grouped with constraints requiring that at least one trace per group is consistent with the learned formula. Our problem is then reduced to an equivalent Pseudo-Boolean Optimization. We evaluate our method against state-of-the-art LTL learning approaches and show that it recovers specifications that more closely align with ground-truth formulas under uncertainty.
CommentsThis paper has been accepted by the ACC2026 (American Control Conference)
