mlsanityData to 7 Oct 2026

Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach

arXiv 2104.15083 ↗First posted 30 Apr 2021cs.LG cs.AI cs.FLcited by 11

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Abstract (arXiv)

We address the problem of inferring descriptions of system behavior using Linear Temporal Logic (LTL) from a finite set of positive and negative examples. Most of the existing approaches for solving such a task rely on predefined templates for guiding the structure of the inferred formula. The approaches that can infer arbitrary LTL formulas, on the other hand, are not robust to noise in the data. To alleviate such limitations, we devise two algorithms for inferring concise LTL formulas even in the presence of noise. Our first algorithm infers minimal LTL formulas by reducing the inference problem to a problem in maximum satisfiability and then using off-the-shelf MaxSAT solvers to find a solution. To the best of our knowledge, we are the first to incorporate the usage of MaxSAT solvers for inferring formulas in LTL. Our second learning algorithm relies on the first algorithm to derive a decision tree over LTL formulas based on a decision tree learning algorithm. We have implemented both our algorithms and verified that our algorithms are efficient in extracting concise LTL descriptions even in the presence of noise.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 7 of its 26 references that are in the map. First posted on arXiv 30 Apr 2021.

Builds on

7 of 26 references.

References · 7

  1. 2018Learning Linear Temporal Propertiescited by 41
  2. 2015Mining Parametric Temporal Logic Properties in Model Based Design for Cyber-Physical Systemscited by 16
  3. 2018Information-Guided Temporal Logic Inference with Prior Knowledgecited by 12
  4. 2020Learning Interpretable Models in the Property Specification Languagecited by 11
  5. 2020Interval Temporal Logic Decision Tree Learningcited by 7
  6. 2016Census Signal Temporal Logic Inference for Multi-Agent Group Behavior Analysiscited by 6
  7. 2019Graph Temporal Logic Inference for Classification and Identificationcited by 6

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Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 4; 2024: 2; 2025: 1.

11 papers.

  1. 2021Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logiccited by 9
  2. 2022Differentiable Inference of Temporal Logic Formulascited by 5
  3. 2021Uncertainty-Aware Signal Temporal Logic Inferencecited by 3
  4. 2023Synthesizing Efficiently Monitorable Formulas in Metric Temporal Logiccited by 3
  5. 2023Pointwise-in-Time Explanation for Linear Temporal Logic Rulescited by 1
  6. 2023On Generating Explanations for Reinforcement Learning Policies: An Empirical Studycited by 1
  7. 2024Learning to Estimate System Specifications in Linear Temporal Logic using Transformers and Mambacited by 1
  8. 2022Symmetry Detection in Trajectory Data for More Meaningful Reinforcement Learning Representationscited by 0
  9. 2023Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inferencecited by 0
  10. 2024Learning Interpretable Classifiers for PDDL Planningcited by 0
  11. 2025Automata Learning of Preferences over Temporal Logic Formulas from Pairwise Comparisonscited by 0