mlsanityData to 7 Oct 2026

Interpretable Apprenticeship Learning with Temporal Logic Specifications

arXiv 1710.10532 ↗First posted 28 Oct 2017eess.SY cs.AI cs.LGcited by 13

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Abstract (arXiv)

Recent work has addressed using formulas in linear temporal logic (LTL) as specifications for agents planning in Markov Decision Processes (MDPs). We consider the inverse problem: inferring an LTL specification from demonstrated behavior trajectories in MDPs. We formulate this as a multiobjective optimization problem, and describe state-based ("what actually happened") and action-based ("what the agent expected to happen") objective functions based on a notion of "violation cost". We demonstrate the efficacy of the approach by employing genetic programming to solve this problem in two simple domains.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Oct 2017.

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Citing papers per year: 2021: 6; 2022: 3; 2023: 2; 2024: 2.

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  1. 2021LTL2Action: Generalizing LTL Instructions for Multi-Task RLcited by 72
  2. 2021A Survey on Interpretable Reinforcement Learningcited by 70
  3. 2021Learning from Demonstrations using Signal Temporal Logiccited by 17
  4. 2022Learning Interpretable Temporal Properties from Positive Examples Onlycited by 12
  5. 2022Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrationscited by 10
  6. 2021Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operatorcited by 5
  7. 2023STL: Surprisingly Tricky Logic (for System Validation)cited by 2
  8. 2024L^*LM: Learning Automata from Examples using Natural Language Oraclescited by 2
  9. 2021Supervised Bayesian Specification Inference from Demonstrationscited by 1
  10. 2021Demonstration Informed Specification Searchcited by 1
  11. 2024Explaining Learned Reward Functions with Counterfactual Trajectoriescited by 1
  12. 2022Learning Probabilistic Temporal Safety Properties from Examples in Relational Domainscited by 0
1 more
  1. 2023Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inferencecited by 0