# Interpretable Apprenticeship Learning with Temporal Logic Specifications

arXiv: https://arxiv.org/abs/1710.10532 · HTML: https://mlsanity.com/paper/1710.10532/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2017.

## Built on by (most cited first, 13 of 13)

- [LTL2Action: Generalizing LTL Instructions for Multi-Task RL](https://mlsanity.com/paper/2102.06858/) (2021)
- [A Survey on Interpretable Reinforcement Learning](https://mlsanity.com/paper/2112.13112/) (2021)
- [Learning from Demonstrations using Signal Temporal Logic](https://mlsanity.com/paper/2102.07730/) (2021)
- [Learning Interpretable Temporal Properties from Positive Examples Only](https://mlsanity.com/paper/2209.02650/) (2022)
- [Temporal Logic Imitation: Learning Plan-Satisficing Motion Policies from Demonstrations](https://mlsanity.com/paper/2206.04632/) (2022)
- [Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator](https://mlsanity.com/paper/2111.04147/) (2021)
- [STL: Surprisingly Tricky Logic (for System Validation)](https://mlsanity.com/paper/2305.17258/) (2023)
- [L^*LM: Learning Automata from Examples using Natural Language Oracles](https://mlsanity.com/paper/2402.07051/) (2024)
- [Supervised Bayesian Specification Inference from Demonstrations](https://mlsanity.com/paper/2107.02912/) (2021)
- [Demonstration Informed Specification Search](https://mlsanity.com/paper/2112.10807/) (2021)
- [Explaining Learned Reward Functions with Counterfactual Trajectories](https://mlsanity.com/paper/2402.04856/) (2024)
- [Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains](https://mlsanity.com/paper/2211.03461/) (2022)
- [Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inference](https://mlsanity.com/paper/2306.09966/) (2023)

## 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.