# Guarded Deep Learning using Scenario-Based Modeling

arXiv: https://arxiv.org/abs/2006.03863 · HTML: https://mlsanity.com/paper/2006.03863/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates](https://mlsanity.com/paper/2405.14058/) (2024)
- [Minimal Multi-Layer Modifications of Deep Neural Networks](https://mlsanity.com/paper/2110.09929/) (2021)
- [Constrained Reinforcement Learning for Robotics via Scenario-Based Programming](https://mlsanity.com/paper/2206.09603/) (2022)
- [Scenario-Assisted Deep Reinforcement Learning](https://mlsanity.com/paper/2202.04337/) (2022)
- [Enhancing Deep Learning with Scenario-Based Override Rules: a Case Study](https://mlsanity.com/paper/2301.08114/) (2023)
- [On Integrating Large Language Models and Scenario-Based Programming for Improving Software Reliability](https://mlsanity.com/paper/2509.09194/) (2025)

## Abstract (arXiv)

Deep neural networks (DNNs) are becoming prevalent, often outperforming manually-created systems. Unfortunately, DNN models are opaque to humans, and may behave in unexpected ways when deployed. One approach for allowing safer deployment of DNN models calls for augmenting them with hand-crafted override rules, which serve to override decisions made by the DNN model when certain criteria are met. Here, we propose to bring together DNNs and the well-studied scenario-based modeling paradigm, by expressing these override rules as simple and intuitive scenarios. This approach can lead to override rules that are comprehensible to humans, but are also sufficiently expressive and powerful to increase the overall safety of the model. We describe how to extend and apply scenario-based modeling to this new setting, and demonstrate our proposed technique on multiple DNN models.