# Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inference

arXiv: https://arxiv.org/abs/2306.09966 · HTML: https://mlsanity.com/paper/2306.09966/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 81 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Learning Linear Temporal Properties](https://mlsanity.com/paper/1806.03953/) (2018)
- [Interpretable Sequence Classification via Discrete Optimization](https://mlsanity.com/paper/2010.02819/) (2020)
- [Interpretable Apprenticeship Learning with Temporal Logic Specifications](https://mlsanity.com/paper/1710.10532/) (2017)
- [Information-Guided Temporal Logic Inference with Prior Knowledge](https://mlsanity.com/paper/1811.08846/) (2018)
- [Learning Interpretable Temporal Properties from Positive Examples Only](https://mlsanity.com/paper/2209.02650/) (2022)
- [Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach](https://mlsanity.com/paper/2104.15083/) (2021)
- [Census Signal Temporal Logic Inference for Multi-Agent Group Behavior Analysis](https://mlsanity.com/paper/1610.05612/) (2016)
- [Graph Temporal Logic Inference for Classification and Identification](https://mlsanity.com/paper/1903.09714/) (2019)
- [Guaranteed Model-Based Fault Detection in Cyber-Physical Systems: A Model Invalidation Approach](https://mlsanity.com/paper/1609.05921/) (2016)
- [Mesh-Based Affine Abstraction of Nonlinear Systems with Tighter Bounds](https://mlsanity.com/paper/1811.02482/) (2018)
- [Data-Driven Model Invalidation for Unknown Lipschitz Continuous Systems via Abstraction](https://mlsanity.com/paper/2003.11662/) (2020)
- [Observability and diagnosability of finite state systems: a unifying framework](https://mlsanity.com/paper/1608.03195/) (2016)
- [Guaranteed Fault Detection and Isolation for Switched Affine Models](https://mlsanity.com/paper/1704.05947/) (2017)

## Abstract (arXiv)

This paper addresses the problem of data-driven model discrimination for unknown switched systems with unknown linear temporal logic (LTL) specifications, representing tasks, that govern their mode sequences, where only sampled data of the unknown dynamics and tasks are available. To tackle this problem, we propose data-driven methods to over-approximate the unknown dynamics and to infer the unknown specifications such that both set-membership models of the unknown dynamics and LTL formulas are guaranteed to include the ground truth model and specification/task. Moreover, we present an optimization-based algorithm for analyzing the distinguishability of a set of learned/inferred model-task pairs as well as a model discrimination algorithm for ruling out model-task pairs from this set that are inconsistent with new observations at run time. Further, we present an approach for reducing the size of inferred specifications to increase the computational efficiency of the model discrimination algorithms.