As unmanned vehicles become more autonomous, it is important to maintain a high level of transparency regarding their behaviour and how they operate. This is particularly important in remote locations where they cannot be directly observed. Here, we describe a method for generating explanations in natural language of autonomous system behaviour and reasoning. Our method involves deriving an interpretable model of autonomy through having an expert ‘speak aloud’ and providing various levels of detail based on this model. Through an online evaluation study with operators, we show it is best to generate explanations with multiple possible reasons but tersely worded. This work has implications for designing interfaces for autonomy as well as for explainable AI and operator training.
|Title of host publication||Proceedings of the 11th International Conference on Natural Language Generation|
|Publisher||Association for Computational Linguistics|
|Number of pages||10|
|Publication status||Published - Nov 2018|
|Event||11th International Conference of Natural Language Generation 2018 - Tilburg University, Tilburg, Netherlands|
Duration: 5 Nov 2016 → 8 Nov 2018
|Conference||11th International Conference of Natural Language Generation 2018|
|Period||5/11/16 → 8/11/18|
Garcia, F. J. C., Robb, D. A., Liu, X., Laskov, A., Patron, P., & Hastie, H. (2018). Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models. In Proceedings of the 11th International Conference on Natural Language Generation (pp. 99-108). Association for Computational Linguistics.