# Towards Explaining Autonomy with Verbalised Decision Tree States

arXiv: https://arxiv.org/abs/2209.13985 · HTML: https://mlsanity.com/paper/2209.13985/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Improving the Interpretability of Deep Neural Networks with Knowledge Distillation](https://mlsanity.com/paper/1812.10924/) (2018)

## Built on by (most cited first, 1 of 1)

- [Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection](https://mlsanity.com/paper/2310.19658/) (2023)

## Abstract (arXiv)

The development of new AUV technology increased the range of tasks that AUVs can tackle and the length of their operations. As a result, AUVs are capable of handling highly complex operations. However, these missions do not fit easily into the traditional method of defining a mission as a series of pre-planned waypoints because it is not possible to know, in advance, everything that might occur during the mission. This results in a gap between the operator's expectations and actual operational performance. Consequently, this can create a diminished level of trust between the operators and AUVs, resulting in unnecessary mission interruptions. To bridge this gap between in-mission robotic behaviours and operators' expectations, this work aims to provide a framework to explain decisions and actions taken by an autonomous vehicle during the mission, in an easy-to-understand manner. Additionally, the objective is to have an autonomy-agnostic system that can be added as an additional layer on top of any autonomy architecture. To make the approach applicable across different autonomous systems equipped with different autonomies, this work decouples the inner workings of the autonomy from the decision points and the resulting executed actions applying Knowledge Distillation. Finally, to present the explanations to the operators in a more natural way, the output of the distilled decision tree is combined with natural language explanations and reported to the operators as sentences. For this reason, an additional step known as Concept2Text Generation is added at the end of the explanation pipeline.