# Inferring Temporal Logic Properties from Data using Boosted Decision Trees

arXiv: https://arxiv.org/abs/2105.11508 · HTML: https://mlsanity.com/paper/2105.11508/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 7 of 7)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Learning Linear Temporal Properties](https://mlsanity.com/paper/1806.03953/) (2018)
- [Specifying User Preferences using Weighted Signal Temporal Logic](https://mlsanity.com/paper/2010.00752/) (2020)
- [Robust Temporal Logic Model Predictive Control](https://mlsanity.com/paper/1511.00347/) (2015)
- [Mining Parametric Temporal Logic Properties in Model Based Design for Cyber-Physical Systems](https://mlsanity.com/paper/1512.07956/) (2015)
- [Information-Guided Temporal Logic Inference with Prior Knowledge](https://mlsanity.com/paper/1811.08846/) (2018)
- [Logic-based Clustering and Learning for Time-Series Data](https://mlsanity.com/paper/1612.07823/) (2016)

## Built on by (most cited first, 2 of 2)

- [STL: Surprisingly Tricky Logic (for System Validation)](https://mlsanity.com/paper/2305.17258/) (2023)
- [Pointwise-in-Time Explanation for Linear Temporal Logic Rules](https://mlsanity.com/paper/2306.13956/) (2023)

## Abstract (arXiv)

Many autonomous systems, such as robots and self-driving cars, involve real-time decision making in complex environments, and require prediction of future outcomes from limited data. Moreover, their decisions are increasingly required to be interpretable to humans for safe and trustworthy co-existence. This paper is a first step towards interpretable learning-based robot control. We introduce a novel learning problem, called incremental formula and predictor learning, to generate binary classifiers with temporal logic structure from time-series data. The classifiers are represented as pairs of Signal Temporal Logic (STL) formulae and predictors for their satisfaction. The incremental property provides prediction of labels for prefix signals that are revealed over time. We propose a boosted decision-tree algorithm that leverages weak, but computationally inexpensive, learners to increase prediction and runtime performance. The effectiveness and classification accuracy of our algorithms are evaluated on autonomous-driving and naval surveillance case studies.