# A Surrogate Model Framework for Explainable Autonomous Behaviour

arXiv: https://arxiv.org/abs/2305.19724 · HTML: https://mlsanity.com/paper/2305.19724/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2023.

## Built on by (most cited first, 4 of 4)

- [Surrogate Modeling and Explainable Artificial Intelligence for Complex Systems: A Workflow for Automated Simulation Exploration](https://mlsanity.com/paper/2510.16742/) (2025)
- [Multimodal Coherent Explanation Generation of Robot Failures](https://mlsanity.com/paper/2410.00659/) (2024)
- [Trust Through Transparency: Explainable Social Navigation for Autonomous Mobile Robots via Vision-Language Models](https://mlsanity.com/paper/2504.05477/) (2025)
- [Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots](https://mlsanity.com/paper/2509.06768/) (2025)

## Abstract (arXiv)

Adoption and deployment of robotic and autonomous systems in industry are currently hindered by the lack of transparency, required for safety and accountability. Methods for providing explanations are needed that are agnostic to the underlying autonomous system and easily updated. Furthermore, different stakeholders with varying levels of expertise, will require different levels of information. In this work, we use surrogate models to provide transparency as to the underlying policies for behaviour activation. We show that these surrogate models can effectively break down autonomous agents' behaviour into explainable components for use in natural language explanations.